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Journal of Software:2014.25(5):913-928

一种基于混合高斯模型的多目标进化算法
周爱民,张青富,张桂戌
(华东师范大学 计算机科学与技术系, 上海 200241;School of Computer Science and Electronic Engineering, University of Essex, UK)
Multiobjective Evolutionary Algorithm Based on Mixture Gaussian Models
ZHOU Ai-Min,ZHANG Qing-Fu,ZHANG Gui-Xu
(Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China;School of Computer Science and Electronic Engineering, University of Essex, UK)
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Received:February 04, 2013    Revised:October 31, 2013
> 中文摘要: 目前,大多数多目标进化算法采用为单目标优化所设计的重组算子.通过证明或实验分析了几个典型的单目标优化重组算子并不适合某些多目标优化问题.提出了基于分解技术和混合高斯模型的多目标优化算法(multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models,简称MOEA/D-MG).该算法首先采用一个改进的混合高斯模型对群体建模并采样产生新个体,然后利用一个贪婪策略来更新群体.针对具有复杂Pareto前沿的多目标优化问题的测试结果表明,对给定的大多数测试题,该算法具有良好的效果.
Abstract:Recombination operators used in most current multiobjective evolutionary algorithms (MOEAs) were originally designed for single objective optimization. This paper demonstrates that some widely used recombination operators may not work well for multiobjective optimization problems (MOPs), and proposes a multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models (MOEA/D-MG). In the algorithm, a reproduction operator based on mixture Gaussian models is used to model the population distribution and sample new trails solutions, and a greedy replacement scheme is then applied to update the population by the new trial solutions. MOEA/D-MG is applied to a variety of test instances with complicated Pareto fronts. The extensive experimental results indicate that MOEA/D-MG is promising for dealing with these continuous MOPs.
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基金项目:国家重点基础研究发展计划(973)(2011CB707104);国家自然科学基金(61273313,61372147) 国家重点基础研究发展计划(973)(2011CB707104);国家自然科学基金(61273313,61372147)
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周爱民,张青富,张桂戌.一种基于混合高斯模型的多目标进化算法.软件学报,2014,25(5):913-928

ZHOU Ai-Min,ZHANG Qing-Fu,ZHANG Gui-Xu.Multiobjective Evolutionary Algorithm Based on Mixture Gaussian Models.Journal of Software,2014,25(5):913-928