In this paper, we propose a new approach to raise the performance of multiobjective particle\nswam optimization. The personal guide and global guide are updated using three kinds of knowledge\nextracted from the population based on cultural algorithms. An epsilon domination criterion has been\nemployed to enhance the convergence and diversity of the approximate Pareto front. Moreover, a\nsimple polynomial mutation operator has been applied to both the population and the non-dominated\narchive. Experiments on two series of bench test suites have shown the effectiveness of the proposed\napproach. A comparison with several other algorithms that are considered good representatives of\nparticle swarm optimization solutions has also been conducted, in order to verify the competitive\nperformance of the proposed algorithm in solve multiobjective optimization problems.
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