A multi-objective evolutionary algorithm based on "exploration" and "exploitation"

被引：0

作者：

Luo B. ^{[1
]}

Zheng J. ^{[1
]}

Zhu Y. ^{[1
,2
]}

Cai Z. ^{[2
]}

机构：

[1] Research Center of Evolutionary Computation and Intelligent System, Xiangtan University

[2] College of Information Science and Engineering, Central South University

来源：

Gaojishu Tongxin/Chinese High Technology Letters | 2010年 / 20卷 / 02期

关键词：

Complex Pareto set; Exploitation; Exploration; Multi-objective evolutionary algorithms; Multi-objective optimization problems;

D O I：

10.3772/j.issn.1002-0470.2010.02.007

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In view of the fact that Pareto Set (PS) of multi-objective optimization problems (MOPs) is often unknown and complex in practice. This paper proposes a multi-objective evolutionary algorithm (MOEA) based on "Exploration" and "Exploitation", named MOEA/2E. This algorithm combines "Exploration" and "Exploitation" in the evolutionary process. It explores new searching areas with evolutionary operators, exploits promising areas effectively with local search and stores optimal individual of a population with elitism. Compared with two popular and efficient MOEAs-NSGA-II and SPEA-II, the experimental results demonstrate that MOEA/2E can obtain Pareto optimal solutions set with better convergence and diversity.

引用

页码：143 / 149

页数：6

共 12 条

[1] Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)
[2] Zitzler E., Thiele L., Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach, IEEE Transactions on Evolutionary Computation, 3, 4, pp. 257-271, (1999)
[3] (2007)
[4] Deb K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evolutionary Computation, 7, 3, pp. 205-230, (1999)
[5] Deb K., Sinha A., Kukkonen S., Multi-objective test problems, linkages, and evolutionary methodologies, Proceedings of Genetic and Evolutionary Computation Conference, Seattle, 2, pp. 1141-1148, (2006)
[6] Zhang Q., Zhou A., Jin Y., RM-MEDA: A regularity model based multiobjective estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation, 12, 1, pp. 41-63, (2008)
[7] 18, 6, pp. 1287-1297, (2007)
[8] Luo B., Zheng J., Xie J., Et al., Dynamic crowding distance a new diversity maintenance strategy for MOEAs, Proceedings of the 4th International Conference on Natural Computation, 1, pp. 580-585, (2008)
[9] van Veldhuizen D.A., Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations, (1999)
[10] Zitzler E., Deb K., Thiele L., Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computation, 8, 2, pp. 117-132, (2003)

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