A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

被引：1

作者：

Shi Chuan Kang Lishan Li Yan Yan ZhenyuState Key Laboratory of Software Engineering Wuhan University Wuhan HubeiChina ^{[430072
]}

机构：

来源：

Wuhan University Journal of Natural Sciences | 2003年 / S1期

关键词：

multi-objective optimal problem; multi-objective optimal evolutionary algorithm; Pareto dominance; tree structure; dynamic space-compressed mutative operator;

D O I：

暂无

中图分类号：

O224 [最优化的数学理论];

学科分类号：

070105 ; 1201 ;

摘要：

<正> Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time.

引用

页码：207 / 211

页数：5

共 4 条

[1]

Introduction to the Special Issue Multicriterion Optimization. Kalyanmay Deb,Jeffrey Horn. Evolutionary Computation . 2000

[2]

Comparison of Multiobjective Evolutinary Algorithms, Empirical Results. Eckart Zitzler,Kalyanmoy Deb,Lothar Thiele. Evolutionary Computation . 2000

[3]

Failure of Pareto-based MOEAs: Does Non-dominated Really MeaNear to Optimal? Proceedings of the 2001 IEEE Congress on Evolutionary Computation. IKEDA Kokolol. Seoul . 2001

[4]

Multiobjective Evolutionary Algorithms, Analyzingthe State-of-the-Art. David A Van,Veldhuizen,Gary B Lamont. Evolutionary Computation . 2000

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