A self-organizing map based hybrid chemical reaction optimization algorithm for multiobjective optimization
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Multiobjective particle swarm optimisation (MOPSO) is faced with convergence difficulties and diversity deviation, owing to combined learning orientations and premature phenomena. In MOPSO, leader selection is an important factor that can enhance the algorithm convergence rate. Inspired by this case, and aimed at balancing the convergence and diversity during the searching procedure, a self-organising map is used to construct the neighbourhood relationships among current solutions. In order to increase the population diversity, an extended chemical reaction optimisation algorithm is introduced to improve the diversity performance of the proposed algorithm. In view of the above, a self-organising map-based multiobjective hybrid particle swarm and chemical reaction optimisation algorithm (SMHPCRO) is proposed in this paper. Furthermore, the proposed algorithm is applied to 35 multiobjective test problems with all Pareto set shape and compared with 12 other multiobjective evolutionary algorithms to validate its performance. The experimental results indicate its advantages over other approaches.
KeywordsMultiobjective optimization Hybrid chemical reaction optimization Self-organizing map Multiobjective particle swarm optimization
The authors would like to thank them for sharing their source codes and providing guidelines to tune the control parameters. This work is partly supported by the National Natural Science Foundation of China under Grant 61272283, 61073091, 61100173, 61403304 and 14JK1511. The statements made herein are solely the responsibility of the author[s].
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