2009 Volume 24 Issue 1 Pages 116-126
This paper presents a Genetic Algorithm (GA) for multi-objective function optimization. To find a precise and widely-distributed set of solutions in difficult multi-objective function optimization problems which have multimodality and curved Pareto-optimal set, a GA would be required conflicting behaviors in the early stage and the last stage of search. That is, in the early stage of search, GA should perform local-Pareto-optima-overcoming search which aims to overcome local Pareto-optima and converge the population to promising areas in the decision variable space. On the other hand, in the last stage of search, GA should perform Pareto-frontier-covering search which aims to spread the population along the Pareto-optimal set. NSGA-II and SPEA2, the most widely used conventional methods, have problems in local-Pareto-optima-overcoming and Pareto-frontier-covering search. In local-Pareto-optima-overcoming search, their selection pressure is too high to maintain the diversity for overcoming local Pareto-optima. In Pareto-frontier-covering search, their abilities of extrapolation-directed sampling are not enough to spread the population and they cannot sample along the Pareto-optimal set properly. To resolve above problems, the proposed method adaptively switches two search strategies, each of which is specialized for local-Pareto-optima-overcoming and Pareto-frontier-covering search, respectively. We examine the effectiveness of the proposed method using two benchmark problems. The experimental results show that our approach outperforms the conventional methods in terms of both local-Pareto-optima-overcoming and Pareto-frontier-covering search.
