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SP-MEC算法的收敛性分析

Study of Convergence of SP-MEC
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摘要 进化算法求解多目标优化问题具有独特的优势。SP-MEC是一种新的利用思维进化算法(MEC)解决多目标优化问题的算法,数值实验结果验证了它的可行性与有效性。文章利用概率论的基本理论对其收敛性进行分析,提出局部Pareto最优解集、局部Pareto最优态集及趋同过程产生的序列强收敛的概念,证明了在满足一定条件下趋同过程产生的序列强收敛于局部Pareto最优态集。 Evolutionary algorithms are well suited for multi-objective optimization problems.Scored Pareto Mind Evolutionary Computation（SP-MEC） is a new Multi-Objective Evolutionary Algorithm（MOEA）,which uses MEC algorithm for multi-objectlve optimization.Feasibillty and efficiency of SP-MEC is illustrated by numerical results.In this paper,the probability theory is used as a tool to analyze convergence of SP-MEC.The concepts of local Pareto optimal solution set and local Pareto optimal state set are presented.Strong convergence of sequence of population generated through operation similartaxis is defined.And it is proved that the sequence of population generated through operation similartaxis strongly converges to local Pareto optimal state set under some conditions.
作者 周秀玲 孙承意
机构地区 北京城市学院人工智能研究所
出处 《计算机工程与应用》 CSCD 北大核心 2006年第24期43-45,52,共4页 Computer Engineering and Applications
基金 国家自然科学基金资助项目(编号:60174002) 北京市教育委员会科技发展计划资助项目(编号:KM200600006001 KM200600006003)
关键词 进化计算 多目标 思维进化计算 收敛性趋同操作 异化操作 evolutionary computation,muhi-objective,mind evolutionary computation,convergence,operation similartaxis, operation dissimilation
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献22

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二级参考文献22

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计算机工程与应用
2006年 第24期

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