Chaos-Genetic Algorithm for Multiobjective Optimization

Chaos-genetic algorithm (CGA) combining local chaotic search and nondominated sorting genetic algorithm for multiobjective optimization is proposed. The method is composed of two stages. The wide search with nondominated sorting genetic algorithm (NSGA-II) is performed at the first searching stage, then the local search with chaotic mutation is performed at the second stage. Moreover, we cancel the limitation of the number of the elitism at each generation and improve the original clustering method. We apply the coverage measure and spread measure to evaluate the performance of the two methods, and obtain more satisfactory results with CGA than that with NSGA-II