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

An improved particle swarm optimizer with inertia weight (PSOIW ) was applied to multi-objective optimization (MOO). In this paper we present a method of multiple particle swarm optimizers with inertia weight (MPSOIW ), which belongs to a kind of the methods of cooperative particle swarm optimization. The crucial idea of the MPSOIW , here, is to reinforce the search ability of the PSOIW 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 distribu- tions 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 ac- quiring better search results. Throughout quantitative analysis to experimental data, we clarify the search characteristics and performance effect of the MPSOIW contrast with that of the PSOIW and MPSOIW.

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