CEC09 Special Session

Special Session & Competition on "Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms", CEC-09, Trondheim, Norway, 18-21 May.

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

Mirrored at: http://dces.essex.ac.uk/staff/qzhang/moeacompetition09.htm by Professor Qingfu Zhang

1. Call for papers and participation in the competition

2. 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.

3. The problems, technical report, codes of test problems in Matlab, C++, final ranking of algorithms, papers, codes of some papers, etc. are available for downloading from: https://github.com/P-N-Suganthan

4. Final Comparison Report/Ranking can be downloaded from: https://github.com/P-N-Suganthan

Accepted Papers & The Associated Codes Available from: https://github.com/P-N-Suganthan

List of Accepted Papers:

1. Minzhong Liu, Xiufen Zou and Yu Chen, Performance Assessment of DMOEA-DD with CEC 2009 MOEA Competition Test Instances.

2. Chih-Ming Chen,Ying-ping Chen and Qingfu Zhang, Enhancing MOEA/D with Guided Mutation and Priority Update for Multi-objective Optimization.

3. Song Gao et al, An Orthogonal Multi-objective Evolutionary Algorithm with Lower-dimensional Crossover.

4. Lin-Yu Tseng and Chun Chen, Multiple Trajectory Search for Unconstrained/Constrained Multi-Objective Optimization.

5. Qingfu Zhang, Wudong Liu and Hui Li, The Performance of a New Version of MOEA/D on CEC09 Unconstrained MOP Test Instances, (The experimental results on UF1-10 were obtained by MATLAB code and results on the other 3 5-objective problems by C++ code.)

6. Yuping Wang et al, A Clustering Multi-objective Evolutionary Algorithm Based on Orthogonal and Uniform Design.

7. Ales Zamuda, Janez Brest, Borko Boskovic, and Viljem Zumer, Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization.

8. Saku Kukkonen and Jouni Lampinen, Performance Assessment of Generalized Differential Evolution3 (GDE3) with a Given Set of Constrained Multi-Objective Optimization Problems.

9. Hai-lin Liu and Xueqiang Li, The multiobjective evolutionary algorithm based on determined weights and sub-regional search.

10. B. Y. Qu and P. N. Suganthan, Multi-objective Evolutionary Programming without Non-domination Sorting is up to Twenty Times Faster.

11. V. L. Huang, S. Z. Zhao, R. Mallipeddi and P. N. Suganthan Multi-objective Optimization Using Self-adaptive Differential Evolution Algorithm.

12. Santosh Tiwari, Georges Fadel, Patrick Koch, and Kalyanmoy Deb, Performance Assessment of the Hybrid Archive-based Micro Genetic Algorithm (AMGA) on the CEC09 Test Problems.

13. Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb and Kaisa Miettinen, Local Search Based Evolutionary Multi-Objective Optimization Algorithm for Constrained and Unconstrained Problems.