A study on two-step search using global-best in PSO for Multi-Objective Optimization Problems

Particle Swarm Optimization (PSO) is one of the most effective search methods in optimization problems. Multi-objective Optimization Problems (MOPs) has been focused on and PSO researches applied to MOPs have been reported. On the other hand, the problem that the search performance using conventional methods for MOPs becomes low is reported in Many-objective Optimization Problems (MaOPs) which have four or more objective functions. This paper proposes two-step searching method based on PSO for MaOPs. In the first step, dividing the population into some groups and each group performs the single objective search for each objective function and the center of them. In the second step, the search is performed to acquire the diversity of Pareto solutions by PSO search with the goal, global-best, based on the solutions acquired in the first step. This paper defines the real coded multi-objective knapsack problem and studies the performance of the proposed method applied to this problem.