Multiple Particle Swarm Optimizers with Inertia Weight for Multi-objective Optimization

An improved particle swarm optimizer with inertia weight (PSOIW alpha) was applied to multi-objective optimization (MOO). In this paper we present a method of multiple particle swarm optimizers with inertia weight (MPSOIW alpha), which belongs to a kind of the methods of cooperative particle swarm optimization. The crucial idea of the MPSOIW alpha, here, is to reinforce the search ability of the PSOIW alpha by the union's power of plural swarms. To demonstrate its effectiveness and search performance, computer experiments on a suite of 2 objective optimization problems are carried out by a weighted sum method. The resulting Pareto-optimal solution distributions corresponding to each given problem indicate that the linear weighted aggregation among the adopted three kinds of dynamically weighted aggregations is the most suitable for acquiring better search results. Throughout quantitative analysis to experimental data, we clarify the search characteristics and performance effect of the MPSOIW alpha contrast with that of the PSOIW alpha and MPSOIW.