Self-controlling dominance area of solutions in evolutionary many-objective optimization

Authors:
Hiroyuki Sato;Hernán E. Aguirre;Kiyoshi Tanaka
Affiliations:
Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan;International Young Researcher Empowerment Center, Shinshu University, Nagano, Japan and Faculty of Engineering, Shinshu University, Nagano, Japan;Faculty of Engineering, Shinshu University, Nagano, Japan
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user-defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective optimization problems (MaOPs). However, to bring out desirable search performance, we have to experimentally find out S that controls dominance area appropriately. Also, there is a tendency to deteriorate the diversity of solutions obtained by CDAS when we decrease S from 0.5. To solve these problems, in this work, we propose a modification of CDAS called self-controlling dominance area of solutions (S-CDAS). In S-CDAS, the algorithm self-controls dominance area for each solution without the need of an external parameter. S-CDAS considers convergence and diversity and realizes a fine grained ranking that is different from conventional CDAS. In this work, we use many-objective 0/1 knapsack problems with m = 4 ∼ 10 objectives to verify the search performance of the proposed method. Simulation results show that SCDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSGA-II, CDAS, IBEAε+ and MSOPS.