Self-Controlling Dominance Area of Solutions in Evolutionary Many-Objective Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Hiroyuki Sato , Hernán E. Aguirre , Kiyoshi Tanaka

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 S-CDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSGA-II, CDAS, IBEAε + and MSOPS. More... »

PAGES

455-465

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-17298-4_49

DOI

http://dx.doi.org/10.1007/978-3-642-17298-4_49

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007645883


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