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A Symbiotic Evolutionary Algorithm for Multi-objective Optimization  

Shin, Kyoung-Seok (전남대학교 산학협력단)
Kim, Yeo-Keun (전남대학교 산업공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.32, no.1, 2007 , pp. 77-91 More about this Journal
Abstract
In this paper, we present a symbiotic evolutionary algorithm for multi-objective optimization. The goal in multi-objective evolutionary algorithms (MOEAs) is to find a set of well-distributed solutions close to the true Pareto optimal solutions. Most of the existing MOEAs operate one population that consists of individuals representing the entire solution to the problem. The proposed algorithm has a two-leveled structure. The structure is intended to improve the capability of searching diverse and food solutions. At the lower level there exist several populations, each of which represents a partial solution to the entire problem, and at the upper level there is one population whose individuals represent the entire solutions to the problem. The parallel search with partial solutions at the lower level and the Integrated search with entire solutions at the upper level are carried out simultaneously. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The optimization problems with continuous variables and discrete variables are used as test-bed problems. The experimental results confirm the effectiveness of the proposed algorithm.
Keywords
Multi-objective Optimization; Multi-objective Evolutionary Algorithm; Symbiotic EA; Pareto Optimal Solutions; Non-dominated Solutions;
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