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A Method of Genetic Algorithm Based Multiobjective Optimization via Cooperative Coevolution  

Lee, Jong-Soo (School of Mechanical Engineering, Yonsei University)
Kim, Do-Young (School of Mechanical Engineering, Yonsei University)
Publication Information
Journal of Mechanical Science and Technology / v.20, no.12, 2006 , pp. 2115-2123 More about this Journal
Abstract
The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.
Keywords
Multiobjective Optimization; Pareto Optimal; Genetic Algorithm; Coevolution; Penalty on Difference;
Citations & Related Records

Times Cited By Web Of Science : 1  (Related Records In Web of Science)
Times Cited By SCOPUS : 2
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