【作者】 周秀玲； 孙承意；

【Author】 ZHOU Xiu-ling,SUN Cheng-yi(Artificial Intelligence Lab,Beijing City College,West Road #9 of North Loop-4,Beijing 100083)

【机构】 北京城市学院人工智能研究所；

【摘要】 Pareto-MEC是一种新的利用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。本文利用概率论的基本理论对其收敛性进行分析。提出局部Pareto最优解集与局部Pareto最优态集概念。给出趋同过程产生的序列强收敛与弱收敛的定义。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。更多还原

【Abstract】 Pareto Mind Evolutionary Computation (Pareto-MEC) is a new Multi-Objective Evolutionary Algorithm (MOEA), which introduces the theory of Pareto into MEC for multi-objective optimization. Feasibility and efficiency of Pareto-MEC are illustrated by numerical results. In this paper the probability theory is used as a tool to analyze convergence of Pareto-MEC. The concepts of local Pareto optimal solution set and local Pareto optimal state set are presented. Strong convergence and weak convergence of sequence of population generated through operation similartaxis are 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.更多还原