Particle Swarm Optimization for Interactive Fuzzy Multiobjective Nonlinear Programming

In recent years, particle swarm optimization (PSO) proposed by Kennedy et al. has been widely used as a general approximate solution method for optimization problems. The authors proposed a revised PSO (rPSO) method incorporating the homomorphous mapping and the multiple stretching technique in order to cope with shortcomings of PSO and showed its efficiency for nonlinear programming problems. In this paper, we construct an interactive fuzzy satisficing method for multiobjective nonlinear programming problems based on the rPSO. In order to obtain better solutions in consideration of the property of multiobjective programming problems, we incorporate the direction to nondominated solutions into the rPSO. Furthermore, the efficiency of the proposed method (MOrPSO) is shown through applications to numerical examples.