摘要： 介绍了一种新的多目标进化算法——Pareto-MEC。将基本MEC和Pareto思想结合起来处理多目标问题。提出了局部Pareto最优解集与局部Pareto最优态集概念，并利用概率论的基本理论证明了趋同过程产生的序列强收敛于局部Pareto最优态集。数值试验验证了Pareto-MEC算法的有效性。
关键词:
进化算法,
多目标优化,
思维进化计算,
收敛性,
趋同操作,
异化操作
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. The concepts of local Pareto optimal solution set and local Pareto optimal state set are presented. And it is proved that the sequence of population generated through operation similartaxis strongly converges to local Pareto optimal state by using the probability theory.
Key words:
Evolutionary computation,
Multi-objective optimization,
Mind evolutionary computation,
Convergence,
Operation similartaxis,
Operation dissimilation
中图分类号:
周秀玲;孙承意. Pareto-MEC算法及其收敛性分析[J]. 计算机工程, 2007, 33(10): 233-236.
ZHOU Xiuling; SUN Chengyi. Pareto-MEC and Its Convergence Analysis[J]. Computer Engineering, 2007, 33(10): 233-236.