다양한 파레토 최적해를 얻기 위한 다목표 유전자 알고리즘의 개선법

초록

This paper proposes a real-coded genetic algorithm to solve multi-objective optimization problems. A proposed algorithm, in this paper, is based on the positive usage of tentative pareto sets obtained at each generation in order to improve efficiencies of Multi-Objective Genetic Algorithm(MOGA). The method is generally used in field of multi-objective optimization problems. We compare the developed multi-objective optimization method to the MOGA method using an experimental example to search pareto optimal sets. We use the simple crossover as a crossover operator and the uniform mutation as a mutation operator to experiment the proposed algorithm and the MOGA.

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