Surveys, Benchmarking, Tutorials in Computational Intelligence
2. G. Wu, R. Mallipeddi, P. N. Suganthan, “Ensemble strategies for population-based optimization algorithms–A survey,” Swarm and Evolutionary Computation, https://doi.org/10.1016/j.swevo.2018.08.015 , 2019
3. P. N. Suganthan, On non-iterative learning algorithms with closed-form solution, Applied Soft Computing 70, 1078-1082, 2018.
4. N Lynn, MZ Ali, PN Suganthan, Population topologies for particle swarm optimization and differential evolution, Swarm and Evolutionary Computation 39, 24-35, 2018.
5. Mandavi, Sedigheh; Rahnamayan, Shahryar; Deb, Kalyanmoy, Opposition based learning: A literature review , Swarm and Evolutionary Computation, Volume: 39 Pages: 1-23 , APR 2018
6. B.Y.Qu, Y.S.Zhu, Y.C.Jiao, M.Y.Wu, P.N.Suganthan, J.J.Liang, “A Survey on Multi-objective Evolutionary Algorithms for the Solution of the Environmental/Economic Dispatch Problems,” Swarm and Evolutionary Computation, Feb 2018. https://doi.org/10.1016/j.swevo.2017.06.002
7. 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, Nov 2017.
8. Rakshit, Pratyusha; Konar, Amit; Das, Swagatam, Noisy evolutionary optimization algorithms - A comprehensive survey , Swarm and Evolutionary Computation, Volume: 33 Pages: 18-45, APR 2017
9. A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, "Computing with the Collective Intelligence of Honey Bees – A Survey," Swarm and Evolutionary Computation, pp. 25-48, Feb 2017. (supplementary file available here) https://doi.org/10.1016/j.swevo.2016.06.001
10. Adam P. Piotrowski, “Review of Differential Evolution population size,” Swarm and Evolutionary Computation, pp. 1-24, Feb 2017.
11. Emrah Hancer, Dervis Karaboga, A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number, pp. 49-67, Swarm and Evolutionary Computation, Feb 2017.
12. Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar, Nithin V. George, “Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review,” pp. 68-84, Swarm and Evolutionary Computation, Feb 2017.
13. Ruchika Malhotra, Megha Khanna, Rajeev R. Raje, “On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions, pp. 85-109, Swarm and Evolutionary Computation, Feb 2017.
14. Michalis Mavrovouniotis, Changhe Li, Shengxiang Yang, “A survey of swarm intelligence for dynamic optimization: Algorithms and applications,” Swarm and Evolutionary Computation, pp. 1-17, April 2017.
15. L. Zhang, P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, DoI: 10.1016/j.ins.2016.01.039, October, 2016. Also the editorial of this special issue on "Randomized Algorithms for Training Neural Networks" in Information Sciences. at https://www.researchgate.net/publication/303392461_Editorial_Randomized_Algorithms_for_Training_Neural_Networks by Dr Dianhui Wang.
16. S. Das, S. S. Mullick, P. N. Suganthan, "Recent Advances in Differential Evolution - An Updated Survey," Swarm and Evolutionary Computation, pp. 1-30, April 2016. https://doi.org/10.1016/j.swevo.2016.01.004
17. 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.
18. 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.
19. P. N. Suganthan, "Numerical Optimization by Nature Inspired Algorithms", keynote lecture at ICHSA 2015, Korea University, Seoul, 19th – 21st Aug. 2015.
20. Musilek, P.; Kromer, P.; Barton, T., Review of nature-inspired methods for wake-up scheduling in wireless sensor networks , Swarm and Evolutionary Computation, Volume: 25 Special Issue: SI Pages: 100-118 Published: DEC 2015.
21. Rammohan Mallipeddi, P. N. Suganthan, "Unit commitment – a survey and comparison of conventional and nature inspired algorithms", Int. J. Bio-Inspired Computation, Vol. 6, No. 2, 2014