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Journal of Mechanical Science and Technology

, Volume 20, Issue 12, pp 2115–2123 | Cite as

A method of genetic algorithm based multiobjective optimization via cooperative coevolution

  • Jongsoo LeeEmail author
  • Doyoung Kim
Article

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.

Key Words

Multi objective Optimization Pareto Optimal Genetic Algorithm Coevolution Penalty on Difference 

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Copyright information

© The Korean Society of Mechanical Engineers (KSME) 2006

Authors and Affiliations

  1. 1.School of Mechanical EngineeringYonsei UniversitySeoulKorea

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