Multi-objective Optimization Using Self-adaptive Differential Evolution Algorithm

被引：92

作者：

Huang, V. L. ^{[1
]}

Zhao, S. Z. ^{[1
]}

Mallipeddi, R. ^{[1
]}

Suganthan, P. N. ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5 | 2009年

关键词：

D O I：

10.1109/CEC.2009.4982947

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

引用

页码：190 / 194

页数：5

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