An Analysis of 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). For further improving its search performance, in this paper, we propose to use a cooperative PSO method called multiple particle swarm optimizers with inertia weight (MPSOIW ) to search. The crucial idea of the MPSOIW , here, is to reinforce the search ability of the PSOIW by the union's power of plural swarms, i.e. distributed processing. To demonstrate the search performance and effect of the proposal, computer experiments on a suite of 2-objective optimization problems are carried out by an aggregation-based manner. The resulting Pareto-optimal solution distributions corresponding to each given problem indicate that the linear weighted aggre- gation among the adopted three kinds of dynamic 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 contrast with that of the original PSOIW, PSOIW , and MPSOIW.

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