Abstract
Particle Swarm Optimization(PSO) algorithm is population-based and it is effective for multi-objective optimization problems.For the convergence of the swarm makes the classical algorithm easily converge to local pareto front,the convergence and diversity of the solution are not satisfactory.This paper proposed an independent dynamic inertia weights method for multi-objective particle swarm optimization(DWMOPSO).It changed each particle's inertia weight according to the evolution speed which was calculated by the value of each particle's best fitness in the history.It improved the probability to escape from the local optima.In comparison with Coello's MOPSO through five standard test functions,the solution of the new algorithm has great improvement both in the convergence to the true Pareto front and diversity.
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