Journal Publications (For more information please send an email to epnsugan@ntu.edu.sg . Latest updates are available form Google Scholar profile once sorted based on year instead of citations)

For selected publications, we make available the codes for academic pursuits. If you make use of these codes, please acknowledge the use of the codes, include the relevant papers in your list of references, and indicate the homepage address (http://www.ntu.edu.sg/home/epnsugan/) in your acknowledgement for the benefit of other researchers.

Updated on 4 Aug 2017

 

1.    L. Zhang, P. N. Suganthan, Benchmarking Ensemble Classifiers with Novel Co-trained Kernel Ridge Regression and Random Vector Functional Link Ensembles, IEEE Computational Intelligence Magazine, 2018. (Codes Available: 2018-CIM-codes)

2.    BY Qu, YS Zhu, YC Jiao, MY Wu, PN Suganthan, JJ Liang, “A Survey on Multi-objective Evolutionary Algorithms for the Solution of the Environmental/Economic Dispatch Problems,” Swarm and Evolutionary Computation, 2018.

3.    MZ Ali, NH Awad, PN Suganthan, RG Reynolds, “An Adaptive Multipopulation Differential Evolution with Dynamic Population Reduction, IEEE Transactions on Cybernetics, accepted, 2017.

4.    BY Qu, JJ Liang, YS Zhu, PN Suganthan, “Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method,” Natural Computing, accepted.

5.    L. Zhang, P. N. Suganthan, "Visual Tracking with Convolutional Random Vector Functional Link Network,", accepted by IEEE Trans on Cybernetics. Codes Videos Available

6.    AW Mohamed, PN Suganthan, Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation, Soft Computing. (Codes Available: 2018-Soft-Comp-2018).

7.    G Wu, W Pedrycz, PN Suganthan, H Li, Using Variable Reduction Strategy to Accelerate Evolutionary Optimization, Applied Soft Computing.

8.    MZ Ali, NH Awad, PN Suganthan, AM Shatnawi, RG Reynolds, An improved Class of Real-Coded Genetic Algorithms for Numerical Optimization, Neurocomputing.

9.    PP Biswas, R Mallipeddi, PN Suganthan, GAJ Amaratunga,  A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network, Applied Soft Computing, 2017

10.  S. Sundar, P. N. Suganthan, C. T. Jin, C. T. Xiang, C. C. Soon, "A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint," Soft Computing, doi: 10.1007/s00500-015-1852-9, 21(5) 1193-1202, 2017.

11.  BY Qu, Q Zhou, JM Xiao, J. J. Liang and P. N. Suganthan, “Large Scale Portfolio Optimization Using Multi-objective Evolutionary Algorithms and Pre-selection Methods", Mathematical Problems in Engineering, Article Number: 4197914, DOI: 10.1155/2017/4197914, 2017.

12.  L Zhang, PN Suganthan, Robust visual tracking via co-trained Kernelized correlation filters, Pattern Recognition 69, 82-93, 2017 (Codes Available).

13.  X. H. Qiu, Y. Ren, P. N. Suganthan, G. Amaratunga, “Empirical Mode Decomposition based Ensemble Deep Learning for Load Demand Time Series Forecasting,” Applied Soft Computing, 54, 246-255, 2017 (Codes Available: 2017-ASOC-EMD-DBN).

14.  PP Biswas, PN Suganthan, GAJ Amaratunga, Optimal power flow solutions incorporating stochastic wind and solar power, Energy Conversion and Management 148, 1194-1207, 2017.

15.  N. Lynn, P. N. Suganthan, “Ensemble particle swarm optimizer,” Applied Soft Computing, 55, 533-548, 2017. (Codes Available: 2017-ASOC-EPSO)

16.  A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, "Computing with the Collective Intelligence of Honey Bees – A Survey," Swarm and Evolutionary Computation, DoI: 10.1016/j.swevo.2016.06.001, Feb 2017. (supplementary file available here)

17.  MF Tasgetiren, D Kizilay, QK Pan, PN Suganthan, “Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion,” Computers & Operations Research 77, 111-126, 2017.

18.  NH Awad, MZ Ali, PN Suganthan, RG Reynolds, “CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization,” Information Sciences 378, 215-241, 2017.

19.  T Jayabarathi, T Raghunathan, BR Adarsh, PN Suganthan, “Economic dispatch using hybrid grey wolf optimizer,” Energy 111, 630-641, 2016.

20.  KZ Gao, PN Suganthan, QK Pan, MF Tasgetiren, A Sadollah, “Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion,” Knowledge-Based Systems 109, 1-16, 2016.

21.  MZ Ali, NH Awad, PN Suganthan, RG Reynolds, “A modified cultural algorithm with a balanced performance for the differential evolution frameworks,” Knowledge-Based Systems 111, 73-86, 2016

22.  NH Awad, MZ Ali, PN Suganthan, E Jaser, “A decremental stochastic fractal differential evolution for global numerical optimization,” Information Sciences 372, 470-491, 2016.

23.  KZ Gao, PN Suganthan, QK Pan, TJ Chua, CS Chong, TX Cai, “An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time,” Expert Systems with Applications 65, 52-67, 2016.

24.  BY Qu, JJ Liang, YS Zhu, ZY Wang, PN Suganthan, “Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm,” Information Sciences 351, 48-66, 2016.

25.  I Fister, PN Suganthan, I Fister Jr, SM Kamal, FM Al-Marzouki, M Perc, “Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution,” Nonlinear Dynamics 84 (2), 895-914, 2016

26.  S. Das, S. S. Mullick, P. N. Suganthan, "Recent Advances in Differential Evolution - An Updated Survey," Swarm and Evolutionary Computation, Vol. 27, pp. 1-30, April, 2016.

27.  L. Zhang, P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, DoI: 10.1016/j.ins.2016.01.039, Volumes 364–365, pp.146–155, Oct, 2016.

28.  Y. Ren, L. Zhang, and P. N. Suganthan, "Ensemble Classification and Regression – Recent Developments, Applications and Future Directions," IEEE Computational Intelligence Magazine, DOI: 10.1109/MCI.2015.2471235, Feb 2016.

29.  Y. Ren, P.  N. Suganthan, N. Srikanth, G. Amaratunga, "Random Vector Functional Link Network for Short-term Electricity Load Demand Forecasting", Information Sciences, Volumes 367–368, pp 1078–1093, Nov. 2016. (Codes Available: 2016-INS-RVFL-Time-Series)  Also from: https://github.com/ron1818/PhD_code/  (RVFL Matlab Codes for Time Series: 2016-RVFL withQScaling.m)

30.  G. H. Wu, R. Mallipeddi, P. N. Suganthan, R. Wang, H. K. Chen, "Differential Evolution with Multi-Population Based Ensemble of Mutation Strategies,"  Information Sciences, DOI: 10.1016/j.ins.2015.09.009, pp. 329-345, 2016. (Codes Available: 2016-INS-MEPDE)

31.  M. Z. Ali, N. H Awad, P.  N. Suganthan, R.  M. Duwairi, R.  G. Reynolds, "A Novel Hybrid Cultural algorithms framework with Trajectory-based Search for Global Numerical Optimization," Information Sciences, Volumes 334–335, 20 March 2016, Pages 219-249.

32.  M. Z. Ali, P. N. Suganthan, R. G. Reynolds, and A. F. Al-Badarneh, "Leveraged Neighborhood-Restructuring in Cultural Algorithms for Solving Real-World Problems," IEEE Trans on Evol. Comp., Volume: 20, Issue: 2, pp. 218 – 231, April 2016, DoI: 10.1109/TEVC.2015.2450018

33.  L. Zhang, P. N. Suganthan, "A Comprehensive Evaluation of Random Vector Functional Link Networks," Information Sciences, DOI: 10.1016/j.ins.2015.09.025, Volumes 367–368, pp. 1094–1105, Nov 2016. (Codes Available: 2016-RVFL-Comp-Eval-Classification)

34.  M. Z. Ali, N. H. Awad, P. N. Suganthan, "Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization," Applied Soft Computing 33, 304-327, 2015.

35.  G. Wu, W. Pedrycz, P. N. Suganthan, R. Mallipeddi, "A variable reduction strategy for evolutionary algorithms handling equality constraints," Vol. 37, Dec. 2015, pp 774–786, Applied Soft Computing. (Codes Available: 2015-ASOC-Constr-Variable-Reduce)

36.  N. Lynn, P. N. Suganthan, "Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation," Swarm and Evolutionary Computation 24, pp. 11-24, 2015. (Codes Available: 2015-SWEVO-HCLPSO)

37.  Y. Ren, P. N. Suganthan, N. Srikanth, "Ensemble methods for wind and solar power forecasting—A state-of-the-art review," Renewable and Sustainable Energy Reviews 50, 82-91, 2015.

38.  K. Z. Gao, P. N. Suganthan, M. F. Tasgetiren, Q. K. Pan, Q. Q. Sun, "Effective Ensembles of Heuristics for Scheduling Flexible Job Shop Problem with New Job Insertion," Vol. 90, Dec. 2015, pp. 107–117, Computers & Industrial Engineering. (Codes Available: 2015-CAIE-1  & 2015-CAIE-2)

39.  K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai, Q. K. Pan, "A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion," Expert Systems with Applications 42 (21), 7652-7663, 2015. (Codes Available: 2015-ESWA-GKZ))

40.  B. Y. Qu, J. J. Liang, Z. Y. Wang, Q. Chen, P. N. Suganthan, "Novel benchmark functions for continuous multimodal optimization with comparative results," Swarm and Evolutionary Computation, DoI10.1016/j.swevo.2015.07.003  2015. (Codes Available)

41.  I Fister, K Ljubič, PN Suganthan, M Perc, "Computational intelligence in sports: Challenges and opportunities within a new research domain,"  Applied Mathematics and Computation 262, 178-186. 2015.

42.  Y. Ren, P. N. Suganthan, N. Srikanth, “A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting,” IEEE, 2014 Trans on Neural Networks and Learning Systems, in press 2015.  Trans on Neural Networks and Learning Systems, DoI: 10.1109/TNNLS.2014.2351391 in press 2015. (Codes Available: 2015-TNNLS-TSTE)

43. L. Zhang, P. N. Suganthan, “Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine,” IEEE Trans on Cybernetics, DoI: 10.1109/TCYB.2014.2366468 , Vol. 45, No. 10, pp. 2165-2176, Oct 2015 (supplementary file available here). (Codes Available: 2015-TCyb-Oblique-RF)

44.  S. Hui, P. N. Suganthan, “Ensemble and Arithmetic Recombination-Based Speciation Differential Evolution for Multimodal Optimization,” IEEE Trans on Cybernetics, DoI: 10.1109/TCYB.2015.2394466 , 2015.

45.  K. Z. Gao, P. N. Suganthan, Q. K. Pan, M. F. Tasgetiren, “An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time,” Int. J. of Production Research, Vol. 53, Issue 19, pp. 5896-5911, 2015. (Codes Available: 2015-IJPR-GKZ))

46.  Y. Ren, P. N. Suganthan, N. Srikanth, “A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods,” IEEE Transactions on Sustainable Energy, 6 (1), 236-244, 2015. (Codes Available: 2015-TNNLS-TSTE)

47.  D. Laha, Y. Ren, P. N. Suganthan , “Modelling of steelmaking process with effective machine learning techniques,”  Expert Systems with Applications 42 (10), 4687-4696, 2015. (Codes Available: 2015-ESWA)

48.  Y Ren, PN Suganthan, “Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting,” Journal of Power and Energy Engineering 2 (04), 176, 2014. (Codes Available: 2014-JPEE-Ren-Ye)

49.   KZ Gao, PN Suganthan, QK Pan, TJ Chua, TX Cai, CS Chong , “Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling,” Information Sciences 289, 76-90, 2014.

50.  R. Mallipeddi, P. N. Suganthan, “Unit commitment–a survey and comparison of conventional and nature inspired algorithms,” Int. Journal of Bio-Inspired Computation 6 (2), 71-90, 2014.

51.  KZ Gao, PN Suganthan, QK Pan, TJ Chua, TX Cai, CS Chong, “Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives,” Journal of Intelligent Manufacturing, DoI: 2014.

52.  J Li, Q Pan, K Mao, PN Suganthan, “Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm,” Knowledge-Based Systems 72, 28-36, 2014.

53.  L. Zhang, P N Suganthan, “Random Forests with Ensemble of Feature Spaces,” Pattern Recognition, 47 (10), 3429-3437, 2014. (Codes Available: 2015-TCyb-Oblique-RF , i.e. a single package for 2 papers)

54.  G Kandavanam, R Mallipeddi, D Botvich, S Balasubramaniam, P. N. Suganthan, “Achieving High Robustness and Performance in QoS-aware Route Planning for IPTV Networks,” Information Sciences, 269, 217-237, 2014. (Codes Available: 2014-INS-QoS-Route)

55.  L Wang, C Fang, PN Suganthan, M Liu, “Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm,” Expert Systems with Applications 41 (5), 2496-2513, 2014.

56.  J Derrac, S García, S Hui, PN Suganthan, F Herrera, “Analyzing convergence performance of evolutionary algorithms: A statistical approach,” Information Sciences 289, 41-58, 2014.  

57.  M F. Tasgetiren, Q. K. Pan, P.N. Suganthan, A. Oner, “A Discrete Artificial Bee Colony Algorithm for the No-Idle Permutation Flow shop Scheduling Problem with the Total Tardiness Criterion,” Applied Mathematical Modeling, Vol. 37, Issues 10–11, 1 June 2013, pp. 6758–6779.

58.  S. Z. Zhao, P. N. Suganthan, “Empirical investigations into the exponential crossover of differential evolution,” Swarm and Evolutionary Computation, DOI: /j.swevo.2012.09.004, Feb 2013, pp. 27-36.

59.  K Gao, Q Pan, P N Suganthan, J Li, “Effective heuristics for the no-wait flow shop scheduling problem with total flow time minimization,” Int. J. of Advanced Manufacturing Technology, .

60.  M F. Tasgetiren, Q. K. Pan, P.N. Suganthan, O. Buyukdagli, “A Variable Iterated Greedy Algorithm with Differential Evolution for the No-Idle Permutation Flow shop Scheduling Problem”, Computers & Operations Research, Vol. 40, Issue 7, July 2013, pp. 1729–1743.

61.  S. Sengupta, S. Das, Md. Nasir, P. N. Suganthan, "Risk Minimization in Biometric Sensor Networks – An Evolutionary Multi-objective Optimization Approach", Soft Computing, . (Codes Available: 2013-Soft-Comp-biometric)

62.  B-Y Qu, P. N. Suganthan, S. Das, "A Distance-Based Locally Informed Particle Swarm Model for Multi-modal Optimization,",  IEEE Trans on Evolutionary Computation, DOI: 10.1109/TEVC.2012.2203138. (Supplementary file), 2013. (Codes Available: 2013-TEC-LIPS)

63.  B. Y. Qu, P. N. Suganthan and J. J. Liang, “Niching particle swarm optimization with local search for multi-modal optimization , Information Sciences, Vol. 197 pp. 131-143, Aug. 2012, DOI: 10.1016/j.ins.2012.02.011. (Codes Available: 2012-INS-niching)

64.  K Z Gao,  P N Suganthan, T J Chua, “Discrete Harmony Search Algorithm for the Disassembly Scheduling Remanufacturing Engineering, Applied Mechanics and Materials, 236, 169-174, 2012

65.  Md. Nasir, D. Maity, S. Das, S.  Sengupta, U. Haldar, P. N.  Suganthan, "A Dynamic Neighborhood Learning based Particle Swarm Optimizer for Global Numerical Optimization," Information Sciences, Vol. 209, pp. 16–36, Nov. 2012. (Codes Available: 2012-INS-dnlpso)

66.  R. Mallipeddi, P. N. Suganthan, "Efficient Constraint Handling for Optimal Reactive Power Dispatch Problem," Swarm and Evolutionary Computation, Vol. 5, pp. 28–36, Aug. 2012. (Codes Available: 2012-SWEVO-Cnostr-Handl-4-power)

67.  S. Z. Zhao, P. N. Suganthan, Q. Zhang, "Decomposition Based Multiobjective Evolutionary Algorithm with an Ensemble of Neighborhood Sizes", IEEE Trans. on Evolutionary Computation, Vol. 16, No. 3, pp. 442-446, June 2012. (Codes Available: 2012-TEC-Ens-MOEAD)

68.  S. Ghosh, S. Das, S. Roy, Sk. Minhazul Islam, and P. N. Suganthan "A Differential Covariance Matrix Adaptation Evolutionary Algorithm for Real Parameter Optimization",  Information Sciences, Vol. 182, No. 1, pp 199-219 Jan. 2012. (Codes Available: 2012-INS-CMA-DE)

69.  B-Y Qu, P N Suganthan, J J Liang, "Differential Evolution with Neighborhood Mutation for Multimodal Optimization,"  IEEE Trans on Evolutionary Computation, DOI: 10.1109/TEVC.2011.2161873. (Supplementary file), Oct 2012. (Codes Available: 2012-TEC-DE-niching)

70.  Sk. Minhazul Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, "An Adaptive Differential Evolution Algorithm with Novel Mutation and Crossover Strategies for Global Numerical Optimization", IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 42, No. 2, pp.  482-500, 2012.

71.  R. Mallipeddi, J. P. Lie, S. G. Razul, P. N. Suganthan, S. C. M. See, "Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look Direction Mismatch", PIER Letters, Vol. 25, pp. 37-46, 2011. (Codes Available: 2011-PIER3-Codes, also refer to PIER2 & PIER1)

72.  R. Mallipeddi, J. P. Lie,  P. N. Suganthan, S. G. Razul, S. C. M. See, "A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry", PIER -119, pp. 381-394, 2011  (Codes Available: 2011-PIER2-Codes, also refer to PIER1 & PIER3)

73.  S. Das, S. Maity, B-Y Qu, P.N. Suganthan, "Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-art", Vol. 1, No. 2,  pp. 71-88, Swarm and Evolutionary Computation, June 2011, DOI: 10.1016/j.swevo.2011.05.005.

74.  S. Sivananaithaperumal, S. Miruna Joe Amali, S. Baskar, P. N. Suganthan, "Constrained self-adaptive differential evolution based design of robust optimal fixed structure controller",  Engineering Applications of Artificial Intelligence, Vol. 24, No. 6, pp. 1084-1093, SEP 2011. (Codes Available: 2011-EAAI-SaDE-robust-controller)

75.  M. F. Tasgetiren, Q-K Pan, P. N. Suganthan and A. H-L Chen, "A Discrete Artificial Bee Colony Algorithm for the Total Flowtime Minimization in Permutation Flow Shops", Information Sciences, Vol. 181, No. 16, pp. 3459-3475, 2011.

76.  S. Z. Zhao, M. I. Willjuice, S. Baskar, P. N. Suganthan, "Multi-objective Robust PID Controller Tuning using Two Lbests Multi-objective Particle Swarm Optimization",  Information Sciences, Vol. 181, No.16, pp. 3323-3335, AUG 2011. (Codes Available: 2011-Ins-PID control)

77.  R. Mallipeddi, J. P. Lie,  P. N. Suganthan, S. G. Razul, S. C. M. See, "Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm", PIER B 29, pp. 157-174, 2011. (Codes Available: 2011-PIER1-Codes, also refer to PIER2 & PIER3)

78.  A. Zhou, B-Y. Qu, H. Li, S-Z. Zhao, P. N. Suganthan, Q. Zhang, "Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art", Swarm and Evolutionary Computation, Vol. 1, No. 1, pp. 32-49, Mar 2011, DOI: 10.1016/j.swevo.2011.03.001.

79.  K. K. Kandaswamy, K-C. Chou, T. Martinetz, S. Möller, P. N. Suganthan, S. Sridharan, P. Ganesan, "AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties," J. of Theoretical Biology, Vol. 270, No. 1, pp. 56-62, FEB. 2011.  

80.  S. Z. Zhao, P. N. Suganthan, Q.-K. Pan, M. F. Tasgetiren, "Dynamic Multi-Swarm Particle Swarm Optimizer with Harmony Search", Expert Systems with Applications, Vol. 38, No. 4, pp. 3735-3742, APR 2011. (Codes Available: 2011-ESWA-DMS-PSO+HS)

81.  Q-K Pan , P. N. Suganthan , J. J. Liang, M. F. Tasgetiren, A Local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem", Expert Systems with Applications, Vol. 38, No. 4, April 2011, pp. 3252-3259.

82.  G. G. Roy, S. Das, P. Chakraborty, and P. N. Suganthan , "Design of Non-Uniform Circular Antenna Arrays using a Modified Invasive Weed optimization Algorithm," IEEE Trans. on Antenna and Propagation, Vol. 59, No. 1, Jan. 2011, pp. 110 - 118. (Codes Available: 2011-TAP-Circular-IWO)

83.  S. Z. Zhao and P. N. Suganthan, S. Das, "Self-adaptive Differential Evolution with Multi-trajectory Search for Large Scale Optimization," Soft Computing, DOI: 10.1007/s00500-010-0645-4. (Codes Available: 2011-Soft-Comp-SaDE-MMTS)

84.  K. K. Kandaswamy, G. Pugalenthi, S. Möller, E. Hartmann, K. Kalies, P. N. Suganthan, T. Martinetz, "Prediction of apoptosis protein locations with Genetic Algorithms and Support-Vector-Machines", Protein Peptide Letters, Vol. 17   No. 12   pp. 1473-1479 DEC 2010.

85.  G. Pugalenthi, K. K. Kandaswamy, P. N. Suganthan, R.Sowdhamini, T. Martinetz and P. Kolatkar, "SMpred: A support vector machine approach to identify structural motifs in protein structure without using evolutionary information", J of Biomolecular Structure & Dynamics, Vol. 28   No. 3   pp. 405-414, DEC 2010.

86.  Q-K. Pan, P. N. Suganthan, L. Wang, L. Gao, R. Mallipeddi, "A Differential Evolution Algorithm with Self-Adapting Strategy and Control Parameters," Computers and Operations Research, Vol. 38 No. 1 pp. 394-408 JAN 2011.

87.  M. F. Tasgetiren, Q. K. Pan, P. N. Suganthan, T. J. Chua, "A Differential Evolution Algorithm for the No-Idle Flowshop Scheduling Problem with Total Tardiness Criterion", International Journal of Production Research, Vol. 49, Issue 16, pp. 5033-5050, 2011.

88.  J-Q Li, Q-K Pan, P. N. Suganthan, T. J. Chua, "A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem", Int. J. of Advanced Manufacturing Technology, Vol. 52,   No. 5-8, pp. 683-697, FEB 2011.

89.  S. Das, P. N. Suganthan, C. A. Coello Coello, "Guest Editorial Special Issue on Differential Evolution", Vol. 15, No. 1, pp. 1-3, DOI: 10.1109/TEVC.2011.2108970.

90.  S. Das and P. N. Suganthan, "Differential Evolution: A Survey of the State-of-the-art", IEEE Trans. on Evolutionary Computation, Vol. 15, No. 1, pp. 4-31, Feb. 2011, DOI: 10.1109/TEVC.2010.2059031. (supplementary reference list cited as [Sxxx] is available).

91.  B. Y. Qu, P. N. Suganthan, "Constrained Multi-Objective Optimization Algorithm with Ensemble of Constraint Handling Methods", Engineering Optimization, Vol. 43, No. 4, p. 403, 2011 (Codes Available: 2011-Eng-Opt-Constr-MODE).

92.  S. Z. Zhao and P. N. Suganthan, “Two-lbests Based Multi-objective Particle Swarm Optimizer”, Engineering Optimization, DOI: 10.1080/03052151003686716, Vol. 43   No. 1   pp. 1-17, 2011 (Codes Available: 2011-Eng-Opt-2LB-MOPSO).

93.  R. Mallipeddi, P. N. Suganthan, Q. K. Pan, M. F. Tasgetiren, "Differential evolution algorithm with ensemble of parameters and mutation strategies" Applied Soft Computing, DOI:10.1016/j.asoc.2010.04.024, Vol. 11   No. 2   pp. 1679-1696, MAR 2011 (Codes Available: 2011-EPSDE-ASOC).

94.  B. Y. Qu, P. N. Suganthan, “Multi-Objective Evolutionary Algorithms based on the Summation of Normalized Objectives and Diversified Selection”, Information Sciences, Vol. 180, No. 17, 1 Sept. 2010, pp. 3170-3181, Doi:10.1016/j.ins.2010.05.013 (Codes Available: 2010-Inf-Sci-Fast-Sort-MODE).

95.  S. Pal, S. Das, A. Basak, and P. N. Suganthan, "Synthesis of difference patterns for monopulse antennas with optimal combination of array-size and number of subarrays - A multiobjective optimization approach", Progress in Electromagnetics Research, PIER B, Vol. 21, pp. 257-280, 2010.

96.  S. Pal, B. Y. Qu, S. Das, and P. N. Suganthan, "Optimal Synthesis of Linear Antenna Arrays with Multi-objective Differential Evolution", Progress in Electromagnetics Research, PIER B, Vol. 21, pp. 87-111, 2010.

97.  A. Anand, G. Pugalenthi, G. B. Fogel, P. N. Suganthan, "An approach for classification of highly imbalanced biological data using weighting and undersampling", Amino Acids, DOI: 10.1007/s00726-010-0595-2, Vol. 39  No. 5   pp. 1385-1391, Nov 2010 (Codes Available: 2010-Amino-A-Imbalanced-Classification).

98.  E. L. Yu, P. N. Suganthan, "Ensemble of niching algorithms", Information Sciences, Vol. 180, No. 15,  pp. 2815-2833, Aug. 2010, DOI: 10.1016/j.ins.2010.04.008.

99.  B. Y. Qu, P. N. Suganthan, “Multi-Objective Differential Evolution with Diversity Enhancement”, Journal of Zhejiang University-SCIENCE A, Vol. 11   No. 7   pp. 538-543 JUL 2010.

100.               G. Pugalenthi, K. K. Kandaswamy, P. N. Suganthan, G. Archunan and R. Sowdhamini, "Identification of functionally diverse lipocalin proteins from sequence information using support vector machine", Amino Acids, Vol. 39   No. 3   pp. 777-783, Aug, 2010, DOI: 10.1007/s00726-010-0520-8.

101.               Q-K Pan, P. N. Suganthan, M. F. Tasgetiren, J. J. Liang, "A Self-Adaptive Global Best Harmony Search Algorithm for Continuous Optimization Problems ", Applied Mathematics and Computation, Vol. 216, No. 3, April 2010, pp. 830-848, DOI: 10.1016/j.amc.2010.01.088 (Codes Available: 2010-AMC-GBest-HS).

102.               R. Mallipeddi, S. Mallipeddi, P. N. Suganthan, “Ensemble strategies with adaptive evolutionary programming”, Information Sciences, vol. 180, no. 9, May 2010, pp. 1571-1581, DOI:10.1016/j.ins.2010.01.007 (Codes Available: 2010-INS-Ens-Str-EP).

103.               Q-K Pan, M. F. Tasgetiren, P. N. Suganthan; T.  J. Chua, "A Discrete Artificial Bee Colony Algorithm for the Lot-streaming Flow Shop Scheduling Problem", Information Sciences, Vol. 181 No. 12, pp. 2455–2468, 2011.

104.                                                                                                                                                                                                  R. Mallipeddi, P. N. Suganthan, “Ensemble of Constraint Handling Techniques”, IEEE Trans. on Evolutionary Computation, Vol. 14, No. 4, pp. 561 - 579 , Aug. 2010, DOI: 10.1109/TEVC.2009.2033582 (Codes Available: 2010-TEC-Ens-Con-EP-DE).

105.               A. Anand, G Pugalenthi, G B Fogel, P.N. Suganthan, "Identification and Analysis of Transcription Factor Family-specific Features Derived from DNA and Protein Information", Pattern Recognition Letters, DOI: 10.1016/j.patrec.2009.10.008, Vol. 31 No. 14, pp. 2097-2100, OCT 2010.

106.                L. Q. Song, M. H. Lim and P. N. Suganthan, “Ensemble of Optimization Algorithms for Solving Quadratic Assignment Problems”, Int. J. of Innovative Computing, Information and Control,  Oct. 2010.

107.               K. Krishna Kumar, G. Pugalenthi, P. N. Suganthan, R. Gangal, "SVMCRYS: An SVM approach for the prediction of protein crystallization propensity from protein", Protein Peptide Letters, Vol. 17, No. 4, pp. 423-430, April 2010.

108.               S. Z. Zhao and P. N. Suganthan, “Multi-objective Evolutionary Algorithm with Ensemble of External Archives”, Int. J. of Innovative Computing, Information and Control, Vol. 6, No. 1, pp 1713-1726, April 2010.

109.               L. Wang, Q-K. Pan, P. N. Suganthan, W. H. Wang, Y. M. Wang, “A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems”, Computers & operations Research, Vol. 37,   No. 3, pp. 509-520, Mar. 2010. DOI:10.1016/j.cor.2008.12.004.

110.               Q.-K. Pan, P. N. Suganthan, J. J. Liang , M. F. Tasgetiren, "A Local-Best Harmony Search Algorithm with Dynamic Subpopulations", Engineering Optimization, Vol. 42, Issue 2, February 2010, pp. 101 - 117. (Codes Available: 2010-EngOpt-LBest-HS)

111.               Q. K. Pan, P. N. Suganthan, T. J. Chua, T. X. Cai, "Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm", Int. J. of Advanced Manufacturing Technology, Vol. 46, No. 9-12   pp. 1229-1237, Feb. 2010, Springer. http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s00170-009-2175-8.

112.               K. K. Kandaswamy, G. Pugalenthi, E. Hartmann, K-U. Kalies, S. Möller, P. N. Suganthan and T. Martinetz, “SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes,” Biochemical and Biophysical Research Communications, Vol. 391, No. 3, pp. 1306-1311, JAN 15 2010.

113.               M. F. Tasgetiren, P. N. Suganthan, Q. K. Pan, "An Ensemble of Discrete Differential Evolution Algorithms for Solving the Generalized Traveling Salesman Problem", Applied Mathematics and Computation, Vol. 215, No. 9, pp. 3356-3368, JAN 2010.

114.               K. Tang,  G. Pugalenthi, P. N. Suganthan, C. J Lanczycki, S. Chakrabarti, "Prediction of functionally important sites from protein sequences using sparse kernel least squares classifiers", Biochemical and Biophysical Research Communications, Vol. 384, Issue 2, pp. 155-159, DOI:10.1016/j.bbrc.2009.04.096, June 2009. 

115.               A. Anand, P. N. Suganthan, "Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates", J. of Theoretical Biology, 259(3):533-540, Aug. 2009, DOI: 10.1016/j.jtbi.2009.04.013. (Codes Available: 2009-Class-Wise-FS)

116.       R. Mallipeddi, P. N. Suganthan, “Differential Evolution Algorithm with Ensemble of populations for Global Numerical Optimization”, OPSEARCH, vol. 46, no. 2, pp. 184-213, June 2009, Springer.

117.               K. Krishna Kumar, G. Pugalenthi , P. N. Suganthan, “DNA-Prot: Identification of DNA binding proteins from protein sequence information using random forest”,  J of Biomolecular Structure & Dynamics, Volume 26, No. 6, pp. 679-686, June 2009.

118.               G. Pugalenthi, K. Tang, P. N. Suganthan and S. Chakrabarti, "Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble", Bioinformatics, 2009 Jan 15;25(2):204-10. (Epub 2008 Nov 27).

119.               A. K. Qin, V. L. Huang, and P. N. Suganthan, "Differential evolution algorithm with strategy adaptation for global numerical optimization", IEEE Trans. on Evolutionary Computations, DOI: 10.1109/TEVC.2008.927706, pp. 398-417, April, 2009 (SaDE - Self-adaptive differential Evolution). (Won "2012 IEEE CIS TEVC Outstanding Paper Award" in June 2011)     (Codes Available: 2009-IEEE-TEC-SaDE)

120.               M. K. Venu, R. Mallipeddi, P. N. Suganthan, Fiber Bragg grating sensor array interrogation using differential evolution, Optoelectronics and Advanced Materials - Rapid Communications, Vol. 2, No. 11, p.682-685, 2008.

121.               A. Anand, G. Pugalenthi, P. N. Suganthan, "Predicting protein structural class by SVM with class-wise optimized features and decision probabilities", J. of Theoretical Biology, DOI:10.1016/j.jtbi.2008.02.031, Vol. 253, Issue 2, July 2008, pp. 375-380 (Codes Available: 2008-JTB-Class_Wise-FS).

122.               G. Pugalenthi, K. Krishna Kumar, P. N. Suganthan and R. Gangal, "Identification of catalytic residues from protein structure using support vector machine with sequence and structural features",  Biochemical and Biophysical Research Communications, Vol 367/3 pp. 630-634, Feb 2008.

123.               G. Pugalenthi, P. N. Suganthan, R. Sowdhamini and S. Chakrabarti, "MegaMotifBase: a database of structural motifs in protein families and superfamilies", Nucleic Acids Res. Vol. 36, pp. D218-221, Jan. 2008.

124.               G. Pugalenthi, E. K. Tang, P. N. Suganthan, G. Archunan, and R. Sowdhamini,  "Machine learning approach for the identification of odorant binding proteins from sequence-derived properties", BMC Bioinformatics, Sep 19, Vol. 8, article  351, 2007 [This paper was selected as a hot paper by Sciencewatch in 2009. http://sciencewatch.com/dr/nhp/2009/09marnhp/09marnhpRamET/ ]

125.       J. J. Liang, C. C. Chan, V. L. Huang and P. N. Suganthan, “Improving the performance of FBG sensor networks using dynamic multi-swarm particle swarm optimizer”, J. of Optoelectronics and Advanced Materials, Rapid-communications, Issue 8, pp. 373-378, 2007.

126.       G. Pugalenthi, P. N. Suganthan, S. Sowdhamini, S. Chakrabarti, “SMotif: A server for structural motifs in proteins”, Bioinformatics 23:637-638 Mar. 2007.

127.       E. K. Tang, P. N. Suganthan and X. Yao, "An Analysis of Diversity Measures", Machine Learning, supplementary materials available here, 65 (1): 247-271 OCT. 2006.

128.       A. K. Qin, P. N. Suganthan and M. Loog, "Generalized Null Space Uncorrelated Fisher Discriminant Analysis for Linear Dimensionality Reduction," Pattern Recognition, Vol. 39, pp. 1805-1808, Sept. 2006.

129.       J. J. Liang, P. N. Suganthan, C. C. Chan and V. L. Huang, “Wavelength detection in FBG sensor network using tree search dynamic multi-swarm particle swarm optimizer”, IEEE Photonics Tech. Lets, 18(12):1305 - 1307, Jun 15, 2006 (Codes Available: 2006-IEEE-PTL-Tree-PSO).

130.       S. Baskar, P. N.  Suganthan, N. Q. Ngo, A. Alphones and R. T. Zheng, “Design of Triangular FBG Filter for Sensor Applications Using Covariance Matrix Adapted Evolution Algorithm,” 260(2):716-722, 15 Apr 2006, Optics Communications (Codes Available: 2006-Opt-Com-I  & 2006-Opt-Com-II).

131.       J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, "Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions", IEEE T. on Evolutionary Computation, Vol. 10, No. 3, pp. 281-295, June 2006 (Codes Available: 2006-TEC-CLPSO).

132.       V. L. Huang, P. N. Suganthan and J. J. Liang, “Comprehensive Learning Particle Swarm Optimizer for Solving Multiobjective Optimization Problems”, Int. J of Intelligent Systems, Vol. 21, No. 2, pp. 209-226, Feb. 2006, USA (Codes Available: 2006-IJIS-MOCLPSO).

133.       J. J. Liang, S. Baskar, P. N. Suganthan and A. K. Qin, “Performance Evaluation of Multiagent Genetic Algorithm”,  Volume 5, Number 1, pp. 83 – 96, Natural Computing, March 2006 (Codes Available: 2006-NACO-MAGA).

134.        E. K. Tang, P. N. Suganthan and X. Yao, “Gene selection algorithms for microarray data using least squares support vector machine”, BMC Bioinformatics, Feb. 2006, 7:95 (Codes Available: 2006-BMC-Bioinfo).

135.               S. Baskar, A. Alphones, P. N. Suganthan and J. J. Liang, “Design of Yagi-Uda Antennas Using Particle Swarm Optimization with new learning strategy”, IEE Proc. on Antenna and Propagation 152(5):340-346 OCT. 2005 (Codes Available: 2005-IEE-Yagi).

136.               Q-Y Zhu, A. K. Qin, P. N. Suganthan, G-B Huang, "Evolutionary Extreme Learning Machine", Pattern Recognition, pp 1759-1763, Vol. 38, No. 10, October 2005.

137.               S. Baskar, A. Alphones, P. N.  Suganthan, N. Q. Ngo and R. T. Zheng, "Design of Optimal Length Low Dispersion FBG filter Using Covariance Matrix Adapted Evolution", IEEE Photonics Technology Letters, 17(10):2119-2121 OCT 2005 (Codes Available: 2005-IEEE-PTL-opt-length-Oct).

138.               A. K.  Qin and P. N. Suganthan, “Enhanced neural gas network for prototype based clustering”, Pattern Recognition, 38(8):1275-1288, August 2005 (Codes Available: 2005-PRJ-ENG)

139.               S. Baskar, A. Alphones and P. N. Suganthan, “Genetic Algorithm Based Design of Reconfigurable Antenna Array with Discrete Phase Shifters,” Microwave and Optical Technology Letters, 45(6):461-465, June 2005 (Codes Available: 2005-Antena-Array-MOTL).

140.       A. K.  Qin and P. N. Suganthan, “Initialization insensitive LVQ algorithm based on cost function adaptation”, 38(5):773-776, Pattern Recognition, May 2005 (Codes Available: 2005-PRJ-H2MLVQ).

141.       E. K. Tang, P. N. Suganthan, X. Yao and A. K. Qin, "Linear Dimensionality Reduction Using Relevance Weighted LDA", 38(4):485-493, Pattern Recognition, April 2005 (Codes Available: 2005-PRJ-Ke-Tang).

142.       A. K.  Qin, P. N. Suganthan and M. Loog, “Uncorrelated Heteroscedastic LDA Algorithm Based on the Weighted Pairwise Chernoff Criterion”, 38(4):613-616, Pattern Recognition, April 2005.

143.       S. Baskar, R. T. Zheng, A. Alphones, N. Q. Ngo and P. N.  Suganthan, “Particle Swarm Optimization for the Design of Low-Dispersion Fiber Bragg Gratings,” IEEE Photonics Technology Letters, 17(3):615-617, March 2005 (Codes Available: 2005-IEEE-PTL-Mar).

144.        A. K. Qin and P. N. Suganthan, “Robust growing neural gas algorithm with application in cluster analysis”, Neural Networks special issue on Recent Developments in Self-Organizing Systems, Vol. 17, No. 8-9, pp. 1135-1148, Oct.-Nov. 2004 (Codes Available: 2004-NN-RNG).

145.                T. Lu and P.N. Suganthan, “An accumulation algorithm for video shot boundary detection”, Multimedia Tools and Applications, 22(1):89-106, Jan. 2004, Kluwer, USA.

146.               A. S. Atukorale, T. Downs, P. N. Suganthan, “Boosting HONG Networks", Neurocomputing, The Netherlands, Vol. 51, pp. 75-86, April 2003.

147.       K. G. Khoo, P. N. Suganthan, “Structural Pattern Recognition using Genetic Algorithms with Specialised Operators", IEEE T. on Systems, Man and Cybernetics - B, USA, 33(1):156-165, Feb. 2003.

148.               X. Cao, P. N. Suganthan, “Video Shot Motion Characterization based on Hierarchical Overlapped Growing Neural Gas Networks", Multimedia Systems, 9(4):378-385, Oct. 2003.

149.                P. N. Suganthan, “Structural pattern recognition using genetic algorithms”, Pattern Recognition, pp. 1883-1893, Vol. 35, No. 9, Sept. 2002, UK.

150.               P. N. Suganthan, “Shape indexing using self-organising maps”, IEEE Trans. on Neural Networks, pp. 835-840, Vol. 13 No. 4 July 2002, USA.

151.               K. G. Khoo, P. N. Suganthan, “Evaluation of genetic operators and solution representations for shape recognition by genetic algorithms”, Pattern Recognition Letters, pp.1589-1597, 2002, The Netherlands.

152.               X. Cao, P. N. Suganthan, “Neural Network based Temporal Video Segmentation", Int. J of Neural Systems, World Scientific Press, Singapore, 2002.

153.               P. N. Suganthan, "Pattern classification using multiple hierarchical overlapped self-organising maps", Pattern Recognition, November, Vol. 34, pp. 2173-2179, 2001, UK.

154.               A. S. Atukorale and P. N. Suganthan, “Hierarchical overlapped Neural-Gas network with application to pattern classification”, Neurocomputing, November 2000.

155.               P. N. Suganthan, “Structure adaptive multilayer overlapped SOMs for labelled pattern classification”, IEEE Trans on Neural Networks, January 1999.

156.               P. N. Suganthan, E. K. Teoh and D. P. Mital, “Hopfield Network with Constraint Parameter Adaptation for Overlapped Shape Recognition, IEEE Trans on Neural Networks, March 1999.

157.               P. N. Suganthan and H. Yan, “Recognition of handprinted Chinese characters by constrained graph matching”, Image and Vision Computing,  Vol. 16, No. 3, 1998.

158.               P. N. Suganthan, H. Yan E. K. Teoh and D. P. Mital, “Optimal encoding of graph homomorphism energy using fuzzy information aggregation operators”, Pattern Recognition, Vol. 31, No. 5, May 1998.

159.               P. N. Suganthan, E. K. Teoh and D. P. Mital, “Optimal mapping of graph homomorphism onto self-organising Hopfield network”, Image and Vision Computing, 15(9):679-694, Sept. 1997.

160.               P. N. Suganthan, E. K. Teoh and D. P. Mital, “Pattern recognition by homomorphic graph matching using Hopfield network”, Image and Vision Computing,  13(1):45-60, 1995.

161.               P. N. Suganthan, E. K. Teoh and D. P. Mital, “A self-organising Hopfield network for attributed relational graph matching”, Image and Vision Computing, 13(1):61-73, Feb., 1995.

162.               P. N. Suganthan, E. K. Teoh and D. P. Mital,  “Pattern recognition by graph matching using the Potts mean field theory network”, Pattern Recognition, pp 997-1009, No. 7, Vol. 28, 1995.

 

Technical Reports  (Send queries to epnsugan@ntu.edu.sg )(Codes Available).

1.             P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger and S. Tiwari, "Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization", Technical Report, Nanyang Technological University, Singapore, May 2005 AND KanGAL Report #2005005, IIT Kanpur, India.

2.             V. L. Huang, P. N. Suganthan A. K. Qin and S. Baskar, "Multiobjective Differential Evolution with External Archive and Harmonic Distance-Based Diversity Measure", Technical Report, Nanyang Technological University, Singapore, December, 2005.

3.             J. J. Liang, T. P. Runarsson, E. Mezura-Montes,  M. Clerc, P. N. Suganthan, C. A. Coello Coello, K. Deb, "Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization”, Technical Rep., Nanyang Technological University, Singapore, 2006.

4.             V. L. Huang, A. K. Qin, K. Deb, E. Zitzler, P. N. Suganthan, J. J. Liang, M. Preuss and S. Huband, "Problem Definitions and Evaluation Criteria for the CEC 2007 Special Session on Multi-objective evolutionary algorithms”, Technical Report, Nanyang Technological University, Singapore, Feb 2007.

5.                  K. Tang, X. Yao, P. N. Suganthan, C. MacNish, Y. P. Chen, C. M. Chen, and Z. Yang, "Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization," Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, & Nanyang Technological University, Singapore, Nov. 2007.

6.             C. Li, S. Yang, T. T. Nguyen, E. L. Yu, X. Yao, Y. Jin, H.-G. Beyer, and P. N. Suganthan, "Benchmark Generator for CEC'2009 Competition on Dynamic Optimization", Technical Report, University of Leicester, University of Birmingham, Nanyang Technological University, September 2008.

7.             Qingfu Zhang, Aimin Zhou, S. Z. Zhao, P. N. Suganthan and Wudong Liu, Santosh Tiwari, "Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition", Technical Report CES-887, University of Essex and Nanyang Technological University, 2008.

8.             K. Tang, Xiaodong Li, P. N. Suganthan, Z. Yang and T. Weise, "Benchmark Functions for the CEC'2010 Special Session and Competition on Large Scale Global Optimization," Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, http://nical.ustc.edu.cn/cec10ss.php, & Nanyang Technological University, 2009.

9.             R. Mallipeddi, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real-Parameter Optimization", Technical Report, Nanyang Technological University, Singapore, 2010.

10.              S. Das, P. N. Suganthan, Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems, Technical Report, Jadavpur University, India & Nanyang Technological University, Singapore, Dec 2010.

11. J. J. Liang, B-Y. Qu, P. N. Suganthan, Alfredo G. Hernández-Díaz, "Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization", Technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and  Technical Report, Nanyang Technological University, Singapore, January 2013.

12. J.. J. Liang, B-Y. Qu, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization", Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and  Technical Report, Nanyang Technological University, Singapore, December 2013.

13.  B. Liu, Q. Chen and Q. Zhang, J. J. Liang, P. N. Suganthan, B. Y. Qu, "Problem Definitions and Evaluation Criteria for Computationally Expensive Single Objective Numerical Optimization", Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and  Technical Report, Nanyang Technological University, Singapore, December 2013.

14.  Q. Chen, B. Liu,  Q. Zhang, J. J. Liang, P. N. Suganthan, B. Y. Qu, "Problem Definition and Evaluation Criteria for CEC 2015 Special Session and Competition on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization", Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and  Technical Report, Nanyang Technological University, Singapore, Nov 2014.

15.  J. J. Liang, B. Y. Qu, P. N. Suganthan, Q. Chen, "Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization", Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and  Technical Report, Nanyang Technological University, Singapore, Nov 2014.

  1. N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu and P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization,"  Technical Report, Nanyang Technological University, Singapore, November 2016. (bound constrained case)
  2. Guohua Wu, R. Mallipeddi, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2017 Competition and Special Session on Constrained Single Objective Real-Parameter Optimization", Technical Report, Nanyang Technological University, Singapore, November 2016. (Single objective, constrained case)

 

Conference Publications (Please send queries to epnsugan@ntu.edu.sg )

Updated on 4 Aug 2017

 

1.     L. Zhang, Jagannadan Varadarajan, P.N. Suganthan, Pierre Moulin, Narendra Ahuja, "Robust Visual tracking with  oblique random forest". (CVPR 2017).[Project Page]

2.     K Rakesh, PN Suganthan, An Ensemble of Kernel Ridge Regression for Multi-class Classification, Procedia Computer Science 108, 375-383

3.     X Qiu, PN Suganthan, GAJ Amaratunga, Short-term Electricity Price Forecasting with Empirical Mode Decomposition based Ensemble Kernel Machines, Procedia Computer Science 108, 1308-1317

4.     PP Biswas, NH Awad, PN Suganthan, MZ Ali, GAJ Amaratunga , Minimizing THD of multilevel inverters with optimal values of DC voltages and switching angles using LSHADE-EpSin algorithm, Evolutionary Computation (CEC), 2017 IEEE Congress on, 77-82

5.     NH Awad, MZ Ali, PN Suganthan, RG Reynolds, AM Shatnawi, A novel differential crossover strategy based on covariance matrix learning with Euclidean neighborhood for solving real-world problems, Evolutionary Computation (CEC), 2017 IEEE Congress on, 380-386 (Codes Available: 2017-CEC-DE-CML-ED)

6.     PP Biswas, PN Suganthan, GAJ Amaratunga, Optimal placement of wind turbines in a windfarm using L-SHADE algorithm, Evolutionary Computation (CEC), 2017 IEEE Congress on, 83-88

7.     NH Awad, MZ Ali, PN Suganthan, Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems, 2017 IEEE Congress on Evolutionary Computation (CEC), 372-379

8.      X Qiu, PN Suganthan, GAJ Amaratunga, Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

9.       C Cubukcuoglu, A Kirimtat, MF Tasgetiren, PN Suganthan, QK Pan, “Multi-objective harmony search algorithm for layout design in theatre hall acoustics,” 2016 IEEE Congress on Evolutionary Computation (CEC), 2280-2287.

10.  NH Awad, MZ Ali, PN Suganthan, E Jaser, “Differential evolution with stochastic fractal search algorithm for global numerical optimization,” 2016 IEEE Congress on Evolutionary Computation (CEC), 3154-3161.

11.  NH Awad, MZ Ali, PN Suganthan, RG Reynolds, “An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems,” 2016 IEEE Congress on Evolutionary Computation (CEC), 2958-2965. (Codes Available: 2016-CEC-Ens-Sin-LSHADE)

12.  A Kirimtat, BK Koyunbaba, I Chatzikonstantinou, S Sariyildiz, et al, “Multi-objective optimization for shading devices in buildings by using evolutionary algorithms,” 2016 IEEE Congress on Evolutionary Computation (CEC), 3917-3924.

13.  M Paldrak, MF Tasgetiren, PN Suganthan, QK Pan, “An ensemble of differential evolution algorithms with variable neighborhood search for constrained function optimization,” 2016 IEEE Congress on Evolutionary Computation (CEC), 2610-2617.

14.  A. C. Palaninathan, Xueheng Qiu, P. N.  Suganthan, “Heterogeneous Ensemble for Power Load Demand Forecasting,” IEEE TENCON 2016, Nov 2016, Singapore.

15.  P. P. Biswas, P. N. Suganthan, and Gehan A. J. Amaratunga. “Optimization of Wind Turbine Rotor Diameters & Hub Heights in a Windfarm using Differential Evolution Algorithm”, in 6th Int. Conf. on Soft Computing for Problem Solving, Thapar University, India, Dec. 2016.

16.  Y Ren, X Qiu, PN Suganthan, G Amaratunga, “Detecting Wind Power Ramp with Random Vector Functional Link (RVFL) Network”, 2015 IEEE Symposium Series on Computational Intelligence, 687-694, Dec, South Africa.

17.  Nandar Lynn, Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan, “Self-adaptive Ensemble Differential Evolution with Sampled Parameter Values for Unit commitment”, SEMCCO 2015, India. .

18.  L Zhang, PN Suganthan, “Visual Tracking with Convolutional Neural Network,” 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

19.  N Lynn, PN Suganthan, “Modified Artificial Bee Colony Algorithm with Comprehensive Learning reinitialization Strategy,” 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

20.  M. Ali, N. Awad, R. Duwairi, J. Albadarneh, R. Reynolds. P. Suganthan, “Cluster-Based Differential Evolution with Heterogeneous Influence for Numerical Optimization,” IEEE Congress on Evolutionary Computation, Japan, May 2015.

21.  N Lynn, PN Suganthan, “Distance Based Locally Informed Particle Swarm Optimizer with Dynamic Population Size,” Proc. of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, 2015.

22.  L Zhang, Y Ren, PN Suganthan, “Towards generating random forests via extremely randomized trees,” 2014 International Joint Conference on Neural Networks (IJCNN), 2645-2652.

23.  X Qiu, L Zhang, Y Ren, P Suganthan, G Amaratunga, “Ensemble deep learning for regression and time series forecasting,” 2014 IEEE Symposium Computational Intelligence in Ensemble Learning (CIEL). (Codes Available: 2014-CIEL2)

24.  Y Ren, X Qiu, P Suganthan, “Empirical mode decomposition based adaboost-backpropagation neural network method for wind speed forecasting,” 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL). (Codes Available: 2014-CIEL)

25.  G Iacca, F Neri, F Caraffini, PN Suganthan, “A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms,” Applications of Evolutionary Computation, 615-626, 2014.

26.  R Mallipeddi, G Wu, M Lee, PN Suganthan, “Gaussian adaptation based parameter adaptation for differential evolution,” 2014 IEEE Congress on Evolutionary Computation (CEC), 1760-1767.

27.  S. Biswas, S. Das, P. N. Suganthan and C. A. C Coello, “Evolutionary Multiobjective Optimization in Dynamic Environments: A Set of Novel Benchmark Functions,” IEEE Congress on Evolutionary Computation, Beijing, PRC, July 2014. (Codes Available: 2014-CEC-Dyn-MOEA)

28.  S. Hui and P. N. Suganthan, “Niching-based Self-adaptive Ensemble DE with MMTS for solving Dynamic Optimization Problems,” IEEE Congress on Evolutionary Computation, Beijing, PRC, July 2014.

29.  M. F. Tasgetiren, S. Das, P. N. Suganthan and F. Neri, “A Differential Evolution Algorithm for Real-Parameter Optimization Problems,” IEEE Congress on Evolutionary Computation, Beijing, July 2014.

30.  M Ali, A. Morghem, J. AlBadarneh, R. Al-Gharaibeh, P. N. Suganthan and R. Reynolds, “Cultural Algorithms Applied to the Evolution of Robotic Soccer Team Tactics: A Novel Perspective, IEEE Congress on Evolutionary Computation, Beijing, PRC, July 2014.

31.  R Mallipeddi, PN Suganthan, “Improved Adaptive Differential Evolution Algorithm with External Archive,” Swarm, Evolutionary, and Memetic Computing, 59-67, India, 2013.

32.  D Basu, S Debchoudhury, KZ Gao, PN Suganthan, “A Novel Improved Discrete ABC Algorithm for Manpower Scheduling Problem in Remanufacturing,” Swarm, Evolutionary, and Memetic Computing, 59-67, India, 2013.

33.  MF Tasgetiren, O Buyukdagli, QK Pan, PN Suganthan, “A General Variable Neighborhood Search Algorithm for the No-Idle Permutation Flowshop Scheduling Problem,” Swarm, Evolutionary, and Memetic Computing, 59-67, India, 2013.

34.  S Debchoudhury, D Basu, KZ Gao, PN Suganthan, “Load Information Based Priority Dependent Heuristic for Manpower Scheduling Problem in Remanufacturing,” Swarm, Evolutionary, and Memetic Computing, 59-67, India, 2013.

35.  K Gao, PN Suganthan, TJ Chua, TX Cai, CS Chong, “Hybrid discrete harmony search algorithm for scheduling re-processing problem in remanufacturing,” GECCO 2013, Netherlands.

36.  R Kundu, R Mukherjee, S Debchoudhury, S Das, PN Suganthan, “Improved CMA-ES with Memory based Directed Individual Generation for Real Parameter Optimization,” IEEE Congress on Evolutionary Computation, Mexico, June 2013.

37.  R Ye, PN Suganthan, N Srikanth, S Sarkar, “A hybrid ARIMA-DENFIS method for wind speed forecasting,” ), 2013 IEEE International Conference on Fuzzy Systems, 2013, India. (Codes Available: 2013-FUZZ)

38.  R Mukherjee, S Debchoudhury, R Kundu, S Das, PN Suganthan, “Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization,” IEEE Congress on Evolutionary Computation, Mexico, June 2013.

39.  S. Sundar, P. N. Suganthan, “A Swarm intelligence approach to flexible job shop scheduling problem with no-wait constraint in remanufacturing”, Proc. Int. Conf. on AI and soft Computing, Poland, June 2013

40.  M. F. Tasgetiren, Q.-K. Pan, P. N Suganthan and I. E. Dizbay, “Metaheuristic Algorithms for the Quadratic Assignment Problem,”Proc. (CIPLS 2013) Computational Intelligence in Production and Logistics Systems, Singapore, April 2013.

41.  K Z Gao, P N Suganthan, T J Chua, “An Enhanced Migrating Birds Optimization Algorithm for No-wait Flow Shop Scheduling Problem,”, Proc. (CIShed 2013), Computational  Intelligence in Scheduling, April 2013, Singapore.

42.  S. Biswas, S. Kundu, S. Das, P. N. Suganthan and B. K. Panigrahi, “Migrating Forager Population in a Multi-population Artificial Bee Colony Algorithm with Modified Perturbation Schemes,” Proc. (SIS 2013), Swarm Intelligence Symposium April 2013, Singapore.

43.  Le Zhang, Ye Ren and P. N. Suganthan, “Instance Based Random Forest with Rotated Feature Space,” Proc. (CIEL 2013), Symposium on Computational Intelligence and Ensemble Learning, April 2013, Singapore.

44.  S. Hui and P. N. Suganthan, “Ensemble Crowding Differential Evolution with Neighborhood Mutation for Multimodal Optimization,” Proc. (CIEL 2013), Symposium on Computational Intelligence and Ensemble Learning, April 2013, Singapore. (Codes Available: 2013-CIEL-SSCI-nhood Crowding EPSDE)

45.  J. Derrac, S. Garcia, S. Hui, F. Herrera and P. N. Suganthan, “Statistical Analysis of Convergence Performance Throughout the Evolutionary Search: A Case Study with SaDE-MMTS and Sa-EPSDE-MMTS,” Proc. (SDE 2013), Symposium on Differential Evolution, April 2013, Singapore (Codes Available: 2013-SDE-SSCI-Sa.EPSDE)

46.  N. Lynn and P. N. Suganthan, “Comprehensive Learning Particle Swarm Optimizer with Guidance Vector Selection,” Proc. (SIS 2013), Swarm Intelligence Symposium April 2013, Singapore.

47.  S. Biswas, S. Kundu, D. Bose, S. Das and P. N. Suganthan, “A Modified Affinity-based Differential Evolution with Restrictive Mutation and Synchronous Population Update,”, Proc. (SDE 2013), Symposium on Differential Evolution, April 2013, Singapore.

48.  Y. Ren, L. Zhang and P. N. Suganthan, “K-Nearest Neighbor based Bagging SVM Pruning,” Proc. (CIEL 2013), Symposium on Computational Intelligence and Ensemble Learning, April 2013, Singapore. (Codes Available: 2013-CIEL)

49.  K. Z. Gao, P. N. Suganthan, and T. J. Chua, “Pareto-based discrete harmony search algorithm for flexible job shop scheduling problems,” Proc. Intelligent Systems Design and Applications (ISDA), Dec 2012.

50.          Y. Ren and P. N. Suganthan, “A Kernel-Ensemble Bagging Support Vector Machine,” Proc. Intelligent Systems Design and Applications (ISDA), Dec 2012

51.          K Z Gao, P N Suganthan, T J Chua “Discrete Harmony Search Algorithm for Dynamic FJSSP in Remanufacturing Engineering”, Proc. Swarm, Evolutionary, and Memetic Computing, pp. 9-16, Dec 2012, BBSR, India.

52.           P N Suganthan, Differential evolution algorithm: recent advances,” Proc. Theory and Practice of Natural Computing, pp. 30-46, Tarragona, Spain, Oct 2012.

53.          Y. Ren, P N Suganthan, “Comparison of Bagging-based Ensemble Classifiers,”, Fusion 2012, Singapore, July 2012. 

54.          B Y Qu, J Liang, P Suganthan, T Chen, “Ensemble of clearing differential evolution for multi-modal optimization,” Advances in Swarm Intelligence, pp. 350-357, Proc. ICSI, June 2012, PR-China.

55.          J Li, Q K Pan, P Suganthan, M Tasgetiren, “Solving Fuzzy Job-Shop Scheduling Problem by a Hybrid PSO Algorithm,” Proc. Swarm and Evolutionary Computation, pp. 275-282, a part of ICAISC 2012, April-May 2012, Poland.

56.          M Tasgetiren, QK Pan, P Suganthan, O Buyukdagli, “A variable iterated greedy algorithm with differential evolution for solving no-idle flowshops,” Swarm and Evolutionary Computation, pp. 128-135, a part of ICAISC 2012, April-May 2012, Poland.  

57.          S. Hui, P. N. Suganthan, “Ensemble Differential Evolution with Dynamic Subpopulations and Adaptive Clearing for solving Dynamic Optimization Problems”, IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012.

58.          J. J. Liang, B-Y. Qu and P. N. Suganthan, “Dynamic Multi-Swarm Particle Swarm Optimization for Multi-Objective Optimization Problems”, IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012.

59.          S-Z. Zhao, P. N. Suganthan, “Comprehensive Comparison of Convergence Performance of Optimization Algorithms based on Nonparametric Statistical Tests”, IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012.

60.          A. Rajasekhar, S. Das and P. N. Suganthan, “Design of Fractional Order Controller for a Servohydraulic Positioning System with Micro Artificial Bee Colony Algorithm”, IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012.

61.          Md Nasir, S. Sengupta, S. Das, P. N. Suganthan, “An improved Multi-objective Optimization Algorithm based on Fuzzy Dominance for Risk Minimization in Biometric Sensor Network”, IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012.

62.          S. Roy, Sk Minhazul Islam, S. Ghosh, S-Z. Zhao, P.N. Suganthan and Swagatam Das, “Design of Two Channel Quadrature Mirror Filter Bank: A Multi-objective approach”, Swarm, Evolutionary and Memetic Computing Conf (SEMCCO 2011), pp. 239-247, LNCS Vol. 7077, DOI: 10.1007/978-3-642-27242-4_28, India.

63.          Sk Minhazul Islam, S. Ghosh, S. Roy, S-Z Zhao, P. N. Suganthan and Swagatam Das, “Synthesis and Design of Thinned Planar Concentric Circular Antenna Array- A Multi-Objective Approach”, Swarm, Evolutionary and Memetic Computing Conf (SEMCCO 2011), pp. 182-190, LNCS Vol. 7077, DOI: 10.1007/978-3-642-27242-4_22, India.

64.          Saurav Ghosh, Subhrajit Roy, Sk Minhazul Islam, Shizheng Zhao, P.N. Suganthan and Swagatam Das, “Non-uniform Circular-shaped Antenna Array Design and Synthesis- A Multi-Objective Approach”, Swarm, Evolutionary and Memetic Computing Conf (SEMCCO 2011), pp. 223-230, LNCS Vol. 7077, DOI: 10.1007/978-3-642-27242-4_26, India.

65.           M. Ankush, A. K. Das, P. Mukherjee, S. Das and P. N. Suganthan, “Modified Differential Evolution with Local Search Algorithm for Real World Optimization”, IEEE Congress on Evolutionary Computation, pp. 1565-1572, New Orleans, USA, June 2011.

66.          R. Mallipeddi, G. Iacca, P. N. Suganthan, F. Neri and E. Mininno, “Ensemble Strategies in Compact Differential Evolution”, IEEE Congress on Evolutionary Computation, pp. 1972 - 1977, New Orleans, USA, June 2011.

67.          R. Mallipeddi and P. N. Suganthan, “Ensemble Differential Evolution Algorithm for CEC2011 Problems”, IEEE Congress on Evolutionary Computation, pp. 1557 - 1564, New Orleans, USA, June 2011.

68.          B. Y. Qu, V. R. Pandi, B. K. Panigrahi, P. N. Suganthan and S. Z. Zhao, “Two Local Best Based Multi Objective Particle Swarm Optimization to Solve Environmental Economic Dispatch Problem”, Proc. ICETECT 2011 India, March 2011.

69.          B. Y. Qu and P. N. Suganthan, “Multi-objective differential evolution based on the summation of normalized objectives and improved selection method,” SDE-2011 IEEE Symposium on Differential Evolution, pp. 1-8, Paris, France, DOI: 10.1109/SDE.2011.5952065, April 2011.  (Codes Available: 2011-SDE-Fast-Sroting-MOEA)

70.          M. F. Tasgetiren, O. Bulut, Q-K Pan, P. N. Suganthan, “A Differential Evolution Algorithm for the Median Cycle Problem”, SDE-2011 IEEE Symposium on Differential Evolution, pp. 1-8, Paris, France, April 2011.

71.          S. Sardar, S. Maity, S. Das, P. N. Suganthan, “Constrained Real Parameter Optimization with a Gradient Repair based Differential Evolution Algorithm”, SDE-2011 IEEE Symposium on Differential Evolution, pp. 1-8, Paris, France, April 2011.

72.          S. Ghosh, S. Roy, M. Islam, S. Das, P. N. Suganthan, “A Differential Covariance matrix Adaptation Evolutionary Algorithm for Global Optimization,” SDE-2011, IEEE Symposium on Differential Evolution, Paris, France, pp. 1-8, April 2011.

73.          G. Iacca, R. Mallipeddi, E. Mininno, F. Neri and P. N. Suganthan, "Super-fit and Population Size Reduction Mechanisms in Compact Differential Evolution", Proc. of the IEEE Symposium on Memetic Computing, pp. 1-8, Paris, April 2011.

74.          G. Iacca, R. Mallipeddi, E. Mininno, F. Neri and P. N. Suganthan, "Global Supervision for Compact Differential Evolution", Proc. of the IEEE Symposium on Differential Evolution, Paris, pp. 1-8, April 2011.

75.          B. Y. Qu, P. N. Suganthan and S. Z. Zhao, “Current Based Fitness Euclidean-distance Ratio Particle Swarm Optimizer for Multi-modal Optimization”, Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010), Kitakyushu, Japan, Dec 15 – 17, 2010

76.              S. Z. Zhao, P. N. Suganthan and B. Y. Qu, Multiobjective Particle Swarm Optimizer with Dynamic epsilon-dominance Sorting”, Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010), Kitakyushu, Japan, Dec 15 – 17, 2010.

77.              R. Mallipeddi and P. N. Suganthan, “Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies", Swarm Evolutionary and Memetic Computing  Conference, LNCS, Vol. 6466, pp. 71-78, Chennai, India 2010. (Codes Available: 2010-SEMCCO-EPSDE)

78.          R. Mallipeddi, Ashu Verma, P. N. Suganthan, Bijaya K. Panigrahi, P. R. Bijwe, Constraint Handling in Transmission Network Expansion Planning, Proc. Swarm, Evolutionary and Memetic Computing Conf, Chennai, India, p. 709-717, Dec. 2010

79.          B. Y. Qu, P. Gouthanan, and P. N. Suganthan, “Dynamic Grouping Crowding Differential Evolution with Ensemble of Parameters for Multi-modal Optimization”, SEMCCO:  Swarm, Evolutionary and Memetic Computing Conf, Chennai, India, Dec 2010.

80.          S. Z. Zhao, P. N. Suganthan and S. Das, “Self-adaptive Differential Evolution with Modified Multi-trajectory Search for CEC'2010 Large Scale Optimization” International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO 2010), Chennai, India, Dec. 16-18, 2010.

81.          B. Y. Qu, V. R. Pandi, B. K. Panigrahi and P. N. Suganthan, “Multi Objective evolutionary programming to Solve Environmental Economic Dispatch Problem”, Proc. ICARCV, Dec 2010.

82.          S. Z. Zhao, B. Y. Qu, M. Willjuice Iruthayarajan, S. Baskar and P. N. Suganthan, “Multi-objective Robust PID Controller Tuning using Multi-objective Differential Evolution” the Eleventh International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore, December, 2010.

83.          S. Z. Zhao, P. N. Suganthan, S. Pal, S. Das and A. Basak, “Multi-Objective Design of Monopulse Antenna with Two-lbests based Multi-objective Particle Swarm Optimizer”, 1st Int. Conf. On Computational Problem-Solving (ICCP2010), Li Jiang, China, Dec 3-5, 2010. (One of the Best 5 Student Paper Awards) (Codes Available: 2010-ICCP-Monopulse)

84.          B. Y. Qu and P. N. Suganthan, “Modified species-based differential evolution with self –adaptive radius for multi-modal optimization,” 1st Int. Conf. On Computational Problem-Solving, Li Jiang, China, Dec 3-5, 2010.

85.          S. Z. Zhao, P. N. Suganthan and S. Das, “Dynamic Multi-Swarm Particle Swarm Optimizer with Sub-regional Harmony Search”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 1983-1990, July 2010. (Codes Available: 2010-CEC-SHS or 2010-CEC-DMS-PSO)

86.  R. Mallipeddi and P. N. Suganthan, “Differential Evolution with Ensemble of Constraint Handling Techniques for Solving CEC 2010 Benchmark Problems”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 1907-1914, July 2010. Upgraded version with feasibility rates available.

87.  B. Y. Qu and P. N. Suganthan, “Novel Multimodal Problems and Differential Evolution with Ensemble of Restricted Tournament Selection”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 3480-3486, July 2010. (Codes Available: 2010-CEC-niching-problems)

88.  M. F. Tasgetiren, P. N. Suganthan, Q-K Pan, R. Mallipeddi and S. Sarman, “An Ensemble of Differential Evolution Algorithms for Constrained Function Optimization”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 967-975, July 2010.

89.  B. Y. Qu and P. N. Suganthan, “Constrained Multi-Objective Optimization Algorithm with Diversity Enhanced Differential Evolution”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 1675-1679, July 2010.

90.  G. G. Roy, P. Chakroborty, S. Z. Zhao, S. Das and P. N. Suganthan, “Artificial Foraging Weeds for Global Numerical Optimization over Continuous Spaces”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 1189-1196, July 2010.

91.  A. Anand, N. R. Pal and P. N. Suganthan, “Integration of Functional Information of Genes in Fuzzy Clustering of Short Time Series Gene Expression Data”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 3002-3009, July 2010.

92.  G. Kandavanam, D. Botvich, S. Balasubramaniam and P. N. Suganthan, “Achieving High Robustness and Performance in Performing QoS-aware Route Planning for IPTV Networks”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 749-756, July 2010.

93.  M. F. Tasgetiren, Q-K Pan, P. N. Suganthan and A. H-L Chen, “A Discrete Artificial Bee Colony Algorithm for the Permutation Flow Shop Scheduling Problem with Total Flow time Criterion”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 137-144, July 2010.

94.    Q-K Pan, M F. Tasgetiren, P N Suganthan and Y-C Liang, “Solving Lot-streaming Flow Shop Scheduling Problems Using a Discrete Harmony Search Algorithm”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 4134-4139, July 2010.

95.          Q. K. Pan, P. N. Suganthan, M. F. Tasgetiren,A Harmony Search Algorithm with Ensemble of Parameter Sets”, IEEE Congress on Evolutionary Computation, pp. 1815-1820, Norway, May 2009.

96.              E. L. Yu, P. N. Suganthan, “Evolutionary Programming with Ensemble of External Memories for Dynamic Optimization”, IEEE Congress on Evolutionary Computation, Norway, pp. 431-438, May 2009. (Codes Available: 2009-CEC-DynEP)

97.          S. Z. Zhao, P. N. Suganthan, “Diversity Enhanced Particle Swarm Optimizer for Global Optimization of Multimodal Problems”, IEEE Congress on Evolutionary Computation, Norway, pp. 590-597, May 2009.

98.          V. L. Huang, S. Z. Zhao, R. Mallipeddi, P. N. Suganthan, “Multi-objective Optimization Using Self-adaptive Differential Evolution Algorithm”, IEEE Congress on Evolutionary Computation, pp. 190-194, Norway, May 2009. (Codes Available: 2009-CEC-MOSaDE)

99.          R. Mallipeddi, P. N. Suganthan, “Diversity Enhanced Adaptive Evolutionary Programming for Solving Single Objective Constrained Optimization Problems”, IEEE Congress on Evolutionary Computation, pp. 2106 - 2113, Norway, May 2009.

100.        B. Y. Qu, P. N. Suganthan, “Multi-objective Evolutionary Programming without Non-domination Sorting is up to Twenty Times Faster”, IEEE Congress on Evolutionary Computation, pp. 2934-2939, Norway, May 2009.

101.        M. F. TasgetirenQuan-Ke Pan, P. N Suganthan and Yun-Chia Liang, “A Differential Evolution Algorithm with Variable Parameter Search for Real-Parameter Continuous Function Optimization ”, IEEE Congress on Evolutionary Computation, Norway, pp. 1247-1254, May 2009.

102.        M. F. Tasgetiren P. N Suganthan, T. J. Chua and A. Al-Hajri, “Differential Evolution Algorithms for the Generalized Assignment Problem”, IEEE Congress on Evolutionary Computation, Norway, pp. 2606-2613May 2009.

103.        R. Mallipeddi, P. N. Suganthan, “Evaluation of Novel Adaptive Evolutionary Programming on Four Constraint Handling Techniques ”, IEEE Congress on Evolutionary Computation, pp. 4045-4052, Hong Kong, June 2008.

104.        R. Mallipeddi, P. N. Suganthan, “Empirical Study on the Effect of Population Size on Differential Evolution”, IEEE Congress on Evolutionary Computation, pp. 3663-3670, Hong Kong, June 2008.

105.        E. L. Yu, P. N. Suganthan, “Empirical Comparison of Niching Methods on Hybrid Composition Functions”, IEEE Congress on Evolutionary Computation, pp. 2194-2201, Hong Kong, June 2008.

106.          Q. K. Pan, M. F. Tasgetiren, Y. C. Liang, P. N Suganthan, “Upper Bounds on Taillard's Benchmark Suite for the No-wait Flowshop Scheduling Problem with Makespan Criterion”, IEEE Congress on Evolutionary Computation, pp. 2955-2961, Hong Kong, June 2008.

107.        T. A. A. Victoire, P. N. Suganthan, “Differential Evolution and Evolutionary Programming for Solving Non-convex Economic Dispatch Problems”, IEEE Congress on Evolutionary Computation,   pp. 1785-1791, Hong Kong, June 2008.

108.          S. Z. Zhao, J. J. Liang, P. N. Suganthan, M. F. Tasgetiren, “Dynamic Multi-swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization”, IEEE Congress on Evolutionary Computation, pp. 3845-3852, Hong Kong, June 2008. (Codes Available: 2008-DMS-PSO)

109.        A. Anand, G. B. Fogel, G. Pugalenthi, P. N. Suganthan, “Prediction of Transcription Factor Families Using DNA Sequence Features”,  P. 3rd IAPR Int. Conf. on Pattern Recognition in Bioinformatics, Oct. 15-17, 2008 Melbourne, AUSTRALIA, Lecture Notes in BIOINFORMATICS   Vol. 5265   pp. 154-164, 2008.

110.        Kandavanam G, Botvich D, Balasubramaniam S, P. N. Suganthan and M. F. Tasgetiren, “A Dynamic Bandwidth Guaranteed Routing Using Heuristic Search for Clustered Topology”, 2nd Int. Symp. on Advanced Networks and Telecommunication Systems, Dec. 15-17, Mumbai, INDIA, pp. 109-111, 2008.

111.                C. Duerr, T. Fuehner and P. N. Suganthan, “LisBON: A framework for parallelisation and hybridisation of optimisation algorithms”, IEEE Congress on Evolutionary Computation, pp. 1717-1724, Sept. 2007, Singapore.

112.         M. F. Tasgetiren, P. N Suganthan and Q.-K. Pan, “A Genetic Algorithm for the Generalized Traveling Salesman Problem”, IEEE Congress on Evolutionary Computation, pp. 2382-2389, Sept. 2007, Singapore.

113.        V. L. Huang, A, K. Qin, P. N. Suganthan and M. F. Tasgetiren, “Multi-objective Optimization based on Self-adaptive Differential Evolution Algorithm”, IEEE Congress on Evolutionary Computation, pp. 3601-3608, Sept. 2007, Singapore. (Codes Available: 2007-CEC-MOSaDE)

114.        T. A. A. Victoire and P. N Suganthan, “Improved MOCLPSO Algorithm for Environmental/Economic Dispatch”, IEEE Congress on Evolutionary Computation, pp. 3072-3076, Sept. 2007, Singapore.

115.        A. Anand, P. N. Suganthan and K. Deb, “A novel fuzzy and multiobjective evolutionary algorithm based gene assignment for clustering short time series expression data”, IEEE Congress on Evolutionary Computation, pp. 297-304, Sept. 2007, Singapore.

116.          G. Kandavanam, D. Botvich, S. Balasubramaniam, P. N. Suganthan and W. Donnelly, “A Multi-layered Solution for supporting ISP traffic demand using Genetic Algorithm”, IEEE Congress on Evolutionary Computation, pp. 2032-2039Sept. 2007, Singapore.

117.        M. F. Tasgetiren, P. N. Suganthan, Q. K. Pan, “A Discrete Particle Swarm Optimization Algorithm for the Generalized Traveling Salesman Problem”, Genetic and Evolutionary Computation Conference, pp. 158-165, London, UK, July 07-11, 2007.

118.          M. F. Tasgetiren, Q. K. Pan, Y. C. Liang, P. N. Suganthan, “A Discrete Differential Evolution Algorithm for the No-Wait Flowshop Scheduling Problem with Total Flow time Criterion”, IEEE Symposium on Computational Intelligence in Scheduling, pp. 251-258, Hawaii, April 2007.

119.          M. F. Tasgetiren, Q-K Pan, Y-C. Liang, P. N Suganthan, “A Discrete Differential Evolution Algorithm for the Total Earliness and Tardiness Penalties with a Common Due Date on a Single-Machine”, IEEE Symposium on Computational Intelligence in Scheduling, pp. 271-278, Hawaii, April 2007.

120.          J. J. Liang and P. N. Suganthan, “Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism”, IEEE Congress on Evolutionary Computation, pp. 9-16, July 2006, Canada. (Codes Available: 2006-CEC-DMS-PSO)

121.          M. F. Tasgetiren and P. N. Suganthan, “A Multi-Populated Differential Evolution Algorithm for Solving Constrained Optimization Problems”, IEEE Congress on Evolutionary Computation, pp. 33-40, July 2006, Canada.

122.          V. L. Huang, A. K. Qin and P. N. Suganthan, “Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization”, IEEE Congress on Evolutionary Computation, pp. 7-24, July 2006, Canada. (Codes Available: 2006-CEC-Constr-SaDE)

123.          A. K. Qin, P. N. Suganthan and M. Loog, “Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis”, 18th International Conference on Pattern Recognition (ICPR 2006), pp. 125-128, Hong Kong, Aug. 2006,

124.          A. Anand, G. Fogel, E. K. Tang and P. N. Suganthan, “Feature selection approach for quantitative prediction of transcriptional activities”, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 57-62, Sept. 2006, Toronto, Canada.

125.          J. J. Liang and P. N. Suganthan, “Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning”, 6th International Conference on Simulated Evolution and Learning, LNCS, Vol. 4247, pp. 213-220, Oct. 2006, China

126.          Y. Li, P. N. Suganthan, et al., “Genetic Algorithms for Silhouette Matching”, 9th International Conference on Control, Automation, Robotics and Vision, pp. 1712-1717, Singapore, Dec. 2006.

127.          A. K. Qin, P. N. Suganthan, C. H. Tay and H. S. Pa, “Personal Identification System based on Multiple Palmprint Features”, 9th International Conference on Control, Automation, Robotics and Vision, pp. 1717-1723, Singapore, Dec. 2006.

128.        A. K. Qin, S. Y. M. Shi, P. N. Suganthan and M. Loog, “Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data”, Proc. American Association for AI, July 2005.

129.          J. J. Liang, and P. N. Suganthan, "Dynamic Multi-Swarm Particle Swarm Optimizer," IEEE Swarm Intelligence Symposium, pp. 124-129, Pasadena, CA, USA, June 2005. (Codes Available: 2005-IEEE-SIS-DMS-PSO & 2008-DMS-PSO-fun)

130.          J. J. Liang, P. N. Suganthan and K. Deb, "Novel composition test functions for numerical global optimization," IEEE Swarm Intelligence Symposium, pp. 68-75, Pasadena, CA, USA, June 2005.  

131.          J. J. Liang and P. N. Suganthan, “Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search," IEEE Congress on Evolutionary Computation, pp. 522-528, Edinburgh, UK, Sept. 2005.      

132.          A. K. Qin and P. N. Suganthan, “Self-adaptive Differential Evolution Algorithm for Numerical Optimization”, IEEE Congress on Evolutionary Computation, pp. 1785-1791, Edinburgh, UK, Sept. 2005.    

133.        J. J. Liang, C. C. Chan, V. L. Huang, & P. N. Suganthan, "Improving the performance of a FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer algorithm." SPIE Symposium on Optics East, Boston, Massachusetts USA, Oct. 2005.

134.          E. K. Tang, P. N. Suganthan and X. Yao, “Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimization”,  IEEE Symposium on Computational Intelligence for Bioinformatics and Computational biology, pp. 9-16, La Jolla, CA, USA, Nov. 2005.

135.        S. Baskar, A. Alphones, P. N. Suganthan, “Design of reconfigurable antenna Array using improved multiagent GAs”, Asia Pacific Microwave Conf. 2004, Dec. New Delhi, India.

136.          A. K. Qin and P. N. Suganthan, “A robust neural gas algorithm for clustering analysis”, International Conference on Intelligent Sensing and Information Processing (ICISIP2004), pp. 342-347, Chennai, India, Jan. 2004.

137.        S. Y. M. Shi, P. N. Suganthan and K. Deb, “Multi class protein fold recognition using multi-objective evolutionary algorithms”, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, USA, Oct. 2004.

138.          A. K. Qin and P. N. Suganthan, “Growing generalized learning vector quantization with local neighborhood adaptation rule”, 2nd IEEE International Conference on Intelligent Systems, pp. 524-529, Sofia, Bulgaria, June 2004.

139.          A. K. Qin and P. N. Suganthan, “Kernel Neural Gas Algorithms with Application to Cluster Analysis”, 17th International Conference on Pattern Recognition (ICPR’04), pp. 617-620, Cambridge, UK Aug. 2004.

140.          A. K. Qin and P. N. Suganthan, “A Novel Kernel Prototype-Based Learning Algorithm”, 17th International Conference on Pattern Recognition (ICPR’04), pp. 621-624, Cambridge, UK Aug. 2004.

141.          S. Baskar, A. Alphones, P. N. Suganthan, “Concurrent PSO and FDR-PSO based reconfigurable Phase-Differentiated Antenna Array Design”, IEEE Congress on Evolutionary Computation, pp. 2173-2179, Oregon, Portland, USA, June 2004.

142.           S. Baskar, and P. N. Suganthan, “A Novel Concurrent Particle Swarm Optimization”, IEEE Congress on Evolutionary Computation, pp. 792-796, Oregon, Portland, USA, June 2004.

143.          E. K. Tang, P. N. Suganthan and X. Yao, “Generalized LDA Using Relevance Weighting and Evolution Strategy”, IEEE Congress on Evolutionary Computation, pp. 2230-2234, Oregon, Portland, USA, June 2004.

144.          E. Lim, K A Toh, P N Suganthan, X. D. Jiang, W. Y. Yau, “Fingerprint image quality analysis”, International Conference on Image Processing (ICIP 2004),  pp. 1241-1244, Singapore, Oct. 2004.

145.          A. K. Qin, P. N. Suganthan and J. J. Liang “A new generalized LVQ algorithm via harmonic to minimum distance measure transition”, IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), pp. 4821-4825, The Hague, The Netherlands, Oct. 2004.

146.          J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Particle Swarm Optimization Algorithms with Novel Learning Strategies” IEEE International Conference on Systems, Man and Cybernetics (SMC2004), pp. 3659-3664, The Hague, The Netherlands, Oct. 2004.

147.          J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Multi-exemplars Particle Swarm Optimization Algorithm”, 11th International Conference on Neural Information Processing (ICONIP2004), Vol. 3316, pp. 230-235, Calcutta, India, Nov., 2004.

148.          E. K. Tang, P. N. Suganthan and X. Yao, "Nonlinear Feature Extraction Using Evolutionary Algorithm", 11th International Conference on Neural Information Processing (ICONIP2004), Vol. 3316, pp. 1014-1019, Calcutta, India, Nov. 2004.

149.          S. Y. M. Shi, P. N. Suganthan, ”Feature Analysis and Classification of Protein Secondary Structure Data”, Springer’s Lecture Notes in Computer Science, Vol. 2714 pp. 1151-1158 (ICANN/ICONIP 2003), Istanbul, Turkey, June 2003.

150.          S. Y. M. Shi, P. N. Suganthan, ”Unsupervised Similarity-based Feature Selection using Heuristic Hopfield Neural Networks”, International Joint Conference on Neural Networks (IJCNN’03), Portland, Oregon, USA, vol. 3, pp 1838-1843, July 2003.

151.          A. S. Atukorale,T. Downs and P. N. Suganthan, "Improving the performance of the HONG network with boosting", Int. Joint Conf. on Neural Networks (IJCNN’02), Vol, 2, pp. 1753 -1756, May 2002.

152.          X. Cao and P. N. Suganthan, "Hierarchical overlapped growing neural gas networks with applications to video shot detection and motion characterization", International Joint Conference on Neural Networks (IJCNN'02), Vol, 2, pp. 1069 -1074, May 2002.

153.          K. G. Khoo and P. N. Suganthan, "Objective function decomposition within genetic algorithm", IEEE Congress on Evolutionary Computation, (CEC '02), Vol. 1, pp. 356 -359, May 2002

154.          K. G. Khoo and P. N. Suganthan, “Clearing Procedure as a Niching Approach to Multiple Relational Graphs Matching, In Proc. SEAL’02, Singapore, November 2002.

155.          A. S. Atukorale, T. Downs, P. N. Suganthan, ”Improving the Performance of the HONG Network with Boosting”, International Joint Conference on Neural Networks (IJCNN’02), Vol. 2, pp. 1753-1756, May 2002.

156.          T. Lu and P. N. Suganthan, “An adaptive cumulation algorithm for video shot detection”, P.  Int. Symposium on Intelligent Multimedia, Video & Speech Processing, in May 2001 in Hong Kong.

157.          K. G. Khoo and P. N. Suganthan, “Multiple Relational Graphs mapping using Genetic Algorithms”, In the Proc. Of the Congress on Evolutionary Computations, May 2001, Korea (INVITED).

158.          P. N. Suganthan, “SHAPESOM”, Proc. Of the Workshop on SOM, June 2001, UK.

159.          X. Cao and P. N. Suganthan, “Video sequence boundary detection using neural GAS networks”, ICANN’01, August 2001, Austria.

160.          H. Yin, P. N. Suganthan and S. M. Krishnan, “Evaluation of distance measures for Image Retrieval Using Self-Organising Map”, ICANN’01, August 2001, Austria.

161.          K. G. Khoo and P. N. Suganthan, “Multiple Relational Graphs Mapping Using Genetic Algorithms With Genetic Engineering Style Operators”, ICICS’01, October 2001, Singapore.

162.          P. N. Suganthan and X. Cao, “Digital video sequence segmentation using self-organising maps”, ICONIP’01, November 2001, China.

163.          P. N. Suganthan, “Shape indexing using relational vectors and neural networks”, ICONIP’01, November 2001, China.

164.          A. S. Atukorale, P. N. Suganthan and T Downs, "On the Performance of the HONG Network for Pattern Classification", P. Int. Joint Conf. on Neural Networks, Como, Italy, paper 590, July 2000.

165.          P. N. Suganthan, “Relational Graph Matching using Self-Organising Maps”, P. ICONIP’2000, Korea, November 2000.

166.          T. Lu and P. N. Suganthan, “The Cumulation Algorithm for Video Shot Detection”, P. ICARCV ‘2000, Singapore, December 2000.

167.          A. S. Atukorale and P. N. Suganthan, “Comparing Performances of Supervised Classifiers”, Proc. Of ICARCV’2000, Singapore, Dec. 2000.

168.          P. N. Suganthan, “Attributed Relational Graph Matching by Neural-Gas Networks”, Proc. Of IEEE Workshop on Neural Networks for Signal Processing, Sydney, Australia, Dec. 2000.

169.          P. N. Suganthan, “Solving jigsaw puzzles using Hopfield network”, Proc. IJCNN99, Washington DC, USA, July 1999.

170.          A. S. Atukorale and P. N. Suganthan, “Combining multiple HONG networks for recognising unconstrained handwritten numerals”, In the Proc. Of IJCNN99, Washington DC, USA, July 1999.

171.          P. N. Suganthan, “Particle swarm optimisation with a neighbourhood operator”, Proc. Of Congress on Evolutionary Computation, Washington DC, USA, July 1999.

172.          A. S. Atukorale and P. N. Suganthan, “Combining Classifiers Based on Confidence Values”, Proc. Of ICDAR'99 Bangalore, India, September 1999.

173.          A. S. Atukorale and P. N. Suganthan, “Comparison of Self-Organizing Maps and Neural Gas Algorithm for Recognizing Unconstrained Handwritten Numerals”, P. IITC'99, Colombo, Sri Lanka, October 1999.

174.          A. S. Atukorale and P. N. Suganthan, “Multiple HONG Network Fusion by Fuzzy Integral”, P. ICONIP'99, Perth, Australia, November 1999.

175.          P. N. Suganthan, “Attributed Relational Graph Matching Using Genetic Algorithms”, P.  of ICAPRDT’99, Calcutta, India, December 1999.

176.          P. N. Suganthan, “Hierarchical self-organising maps”, Proc. of ACNN'98, Brisbane, February 1998.

177.          P. N. Suganthan, “Structure adaptive multilayer overlapped SOMs with supervision for handprinted digit classification”, Proc. of the IJCNN'98, Alaska, USA, May 1998.

178.          P. N. Suganthan and N.R.Pal, “Pattern classification using multiple SOMs”, P. ICONIP'98, Kitakyushu, October 1998.

179.          P. N. Suganthan, “Hierarchical Overlapped SOM Based Multiple Classifiers Combination”, Proc. of the 5th Int. Conf. on Automation, Robotics, Control and Vision, Singapore, December 1998.

180.          A. S. Atukorale and P. N. Suganthan, “An efficient Neural Gas network for classification”, Proc. of the 5th  Int. Conf. on Automation, Robotics, Control and Vision, Singapore, December 1998.

181.          J. J. Stoll and P. N. Suganthan, “Face recognition using SOMs”, Proc. of the 5th Int. Conf. on Automation, Robotics, Control and Vision, Singapore, December 1998.

182.          P. N. Suganthan and H. Yan, “Stroke-based merged handwritten Kanji characters recognition”, Proc. of the Real World Computing Symposium, Japan, January 1997.

183.          P. N. Suganthan, “Structure adaptive multilayer SOM with partial supervision for numeral recognition”, Proc of the ICONIP’97, Dunedin, New Zealand, November 1997.

184.          P. N. Suganthan and H. Yan, “Handwritten Chinese character recognition by ARG matching using self-organising Hopfield network”, P. IEEE Int. Conf. on Neural Networks, Washington DC, June 1996.

185.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “On mapping of ARG matching onto neural networks”, P. IEEE Systems, Man and Cybernetics Conference, Canada, October 1995.

186.          P.N. Suganthan, E. K. Teoh and D. P. Mital, “Homomorphic graph matching using self-organising Hopfield network”, IEE Artificial Neural Networks Conference, June 1995, Cambridge, UK.

187.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “Learning critical temperature for homomorphic ARG matching by self-organising Hopfield network”, P. IEEE Int. Conf. on Neural Networks, Perth, Australia, December 1995.

188.          P. N. Suganthan, E. K. Teoh, D. P. Mital, “Fuzzy connectives based optimal mapping of homomorphic ARG matching onto self-organising Hopfield network”, P. IEEE Int. Conf. on Neural Networks, Perth, Australia, December 1995.

189.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “Shape recognition using Potts MFT neural networks”, P. of the Asian Conference on Computer Vision, Singapore, December 1995.

190.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “Programming Hopfield network for relational homomorphism”, P. 1994 IEEE TENCON, Singapore.

191.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “On attributed relational graph matching using Hopfield network”, P. 1994 European Conf. on Artificial Intelligence, The Netherland, August 1994.

192.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “Programming Hopfield network for object recognition”, P. of 1993 IEEE Systems, Man and Cybernetics Conference, France.

193.          P. N. Suganthan, E. K. Teoh and D. P. Mital, “Flexible circuits recognition using multilayer back propagation network”, P. of 1993 IEEE IECON Conference, Hawaii.