多目标粒子群优化中优区控制分析

A. B. Carvalho, A. Pozo
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引用次数: 2

摘要

近年来,人们对应用粒子群优化来解决各种问题,特别是多目标问题的兴趣越来越大。这种元启发式方法特别适用于解决现实生活中的问题,但与其他多目标元启发式方法一样,在处理具有许多目标的问题时(通常是三个以上)存在一些局限性。近年来,为了避免基于Pareto优势的多目标进化算法对多目标问题的搜索能力下降,提出了一些多目标技术。本文研究了多目标解的优势区域控制在多目标粒子群优化中的作用。通过一些质量指标的分析和统计检验,实证分析了CDAS技术对多目标粒子群算法在多目标场景下的收敛性和多样性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing the control of dominance area of solutions in particle swarm optimization for many-objective
The interest in the application of particle swarm optimization to solve different problems, especially multi-objective problems, grew in recent years. This metaheuristic is particularly suitable to solve real life problems, but like other multi-objective metaheuristics, has some limitations when dealing with problems with many objectives, typically more than three. Recently, some many-objective techniques were proposed to avoid the deterioration of the search ability of Pareto dominance based multi-objective evolutionary algorithms for many-objective problems. This work presents a study of the influence of the many-objective technique called the control of dominance area of solutions (CDAS) in multi-objective particle swarm optimization. It is presented an empirical analysis to identify the influence of the CDAS technique on the convergence and the diversity of a multi-objective PSO algorithm in many-objective scenarios through the analysis of some quality indicators and statistical tests.
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