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首页> 外文期刊>IAENG Internaitonal journal of computer science >An Analysis of Multiple Particle Swarm Optimizers with Inertia Weight for Multi-objective Optimization
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An Analysis of Multiple Particle Swarm Optimizers with Inertia Weight for Multi-objective Optimization

机译：具有惯性权重的多粒子群优化器的多目标优化分析

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摘要

An improved particle swarm optimizer with inertia weight (PSOIWα) 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 (MPSOIWα) to search. The crucial idea of the MPSOIWα, here, is to reinforce the search ability of the PSOIWα 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 aggregation 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 MPSOIWα contrast with that of the original PSOIW, PSOIWα, and MPSOIW.
机译：将改进的具有惯性权重的粒子群优化器（PSOIWα）应用于多目标优化（MOO）。为了进一步提高其搜索性能，在本文中，我们建议使用一种称为PSO的协作PSO方法，该算法具有惯性权重（MPSOIWα）。这里，MPSOIWα的关键思想是通过多个群体的联合的力量，即分布式处理，来增强PSOIWα的搜索能力。为了证明该建议的搜索性能和效果，通过基于聚集的方式对一组2目标优化问题进行了计算机实验。得到的对应于每个给定问题的帕累托最优解分布表明，所采用的三种动态加权聚合中的线性加权聚合最适合于获取更好的搜索结果。通过对实验数据的定量分析，我们弄清了MPSOIWα与原始PSOIW，PSOIWα和MPSOIW的搜索特征和性能效果。

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