Special Session & Competition on Real-Parameter Single Objective (Expensive) Optimization at

CEC-2014, Beijing, PR-China 6-11 July 2014

If you face any difficulties, please inform me ( epnsugan@ntu.edu.sg  ).

  1. Call for papers

  2. 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.

  3. 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.

  4. To download, Matlab, JAVA, C & Data files. please follow the link:  http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared Documents/Forms/AllItems.aspx to CEC2014 folder.

  5. Comparison of Algorithms and codes of top performing algorithms are available in CEC 2014 folder at this link:  http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared Documents/Forms/AllItems.aspx

Important Points:

** Solutions should be uniformly initialized within the whole search space.

** Search must be conducted within the defined search ranges. Solutions out side of the defined search ranges are treated as invalid solutions.


Published Papers (Corresponding to TR @ #2)


Testing United Multi-Operator Evolutionary Algorithms on the CEC2014 Real-Parameter Numerical Optimization.  By Saber M. Elsayed, Ruhul A. Sarker, Daryl L. Essam and Noha M. Hamza 



Improving the Search Performance of SHADE Using Linear Population Size Reduction. By Ryoji Tanabe and Alex S. Fukunaga (Winner of the Competition)


A Differential Evolution with Replacement Strategy for Real-Parameter Numerical Optimization. By ChangJian Xu, Han Huang, and ShuJin Ye


Memetic Differential Evolution Based on Fitness Euclidean-Distance Ratio.  By  B. Y. Qu, J. J. Liang,  J. M. Xiao, Z. G. Shang


Partial Opposition-Based Adaptive Differential Evolution Algorithms: Evaluation on the CEC 2014 Benchmark Set for Real-parameter Optimization. By Zhongyi Hu, Yukun Bao, and Tao Xiong


Differential Evolution Strategy based on the Constraint of Fitness Values Classification.  By  Zhihui Li, Zhigang Shang, B. Y. Qu, J. J. Liang


Evaluating the Mean-Variance Mapping Optimization on the IEEE-CEC 2014 Test Suite. By István Erlich, José L. Rueda, and Sebastian Wildenhues


Influence of regions on the memetic algorithm for the CEC’2014 Special Session on Real-Parameter Single Objective Optimisation. By Daniel Molina Benjamin Lacroix Francisco Herrera


Real-Parameter Optimization with OptBees. By Renato Dourado Maia, Leandro Nunes de Castro, and Walmir Matos Caminhas


Bandits attack function optimization.  By Philippe Preux and R´emi Munos and Michal Valko


The same as above.


Fireworks Algorithm with Differential Mutation for Solving the CEC 2014 Competition Problems.  By  Chao Yu, Lingchen Kelley, Shaoqiu Zheng, and Ying Tan


An Evolutionary Algorithm Based on Covariance Matrix Leaning and Searching Preference for Solving CEC 2014 Benchmark Problems.  By Lei Chen, Hai-Lin Liu, Zhe Zheng, Shengli Xie


Gaussian Adaptation based Parameter Adaptation for Differential Evolution.  By  R. Mallipeddi, Guohua Wu, Minho Lee and P. N. Suganthan


Non-Uniform Mapping in Real-Coded Genetic Algorithms.   By  Dhebar Yashesh, Kalyanmoy Deb and Sunith Bandaru


Differential Evolution with Rotation-Invariant Mutation and Competing-Strategies Adaptation.   By  Petr Bujok, Josef Tvrdık and Radka Polakov

DE_b6e6rl with restart

Controlled Restart in Differential Evolution Applied to CEC2014 Benchmark Functions.  By   Radka Polakova, Josef Tvrdık and Petr Bujok


Published Papers (Corresponding to TR @ #3) 

WA (available as a technical report)

An Approach to Solve Computationally Expensive Optimization Problems of CEC‐2014 Without Approximation.  By Md Asafuddoula, Tapabrata Ray


A Surrogate‐SA DE assisted Differential Evolution algorithm with Dynamic Parameters Selection for Solving Expensive Optimization Problems. By SaberM. Elsayed, T. Ray, Ruhul A. Sarker


A Hybrid Surrogate based Algorithm (HSBA) to Solve Computationally Expensive Optimization Problems. By Hemant Kumar Singh, Amitay Isaacs, Tapabrata Ray


Evaluating the performance of Group Counselling Optimizer on CEC 2014 problems for Computational Expensive Optimization.  By Subhodip Biswas, Mohammad A. Eita, Swagatam Das, Athanasios V. Vasilakos

MVMO (Winner)

Solving the IEEE CEC 2014 Expensive Optimization Test Problems by Using Single‐Particle MVMO. By István Erlich, José L. Rueda, SebastianWildenhues


SO‐MODS: Optimization for High Dimensional Computationally Expensive Multi‐Modal Functions with Surrogate Search.   By   Tipaluck Krityakierne, Juliane Müller, Christine A. Shoemaker


Paper Submission

Authors must strictly follow the manuscript preparation instructions:  http://www.ieee-wcci2014.org/Paper-Submission.htm


When submitting, please make sure you select "SS42. EC42: Single Objective Numerical Optimization" as the "Main Research Topic" for all papers making use of these benchmarks. Each submission should include either the standard or expensive problems (i.e. these 2 sets of problems should not be combined into one paper).

Paper Submission Deadline

We will follow the extended deadlines as determined by WCCI 2014.


Request for Feedback

If you have suggestions to improve the technical report or if you find a potential bug in the codes, please inform us.




Software packages (Matlab, JAVA, C) & data files of the single objective optimization were updated on 23rd of Dec.

Software packages of expensive (or reduced fitness evaluations based) single objective optimization were updated on 30th of Dec.