Robust Differential Evolution for Solving Numerical Optimization Problems

被引：0

作者：

Lin, Chun-Ling ^{[1
]}

Hsieh, Sheng-Ta ^{[2
]}

Wu, Huang-Lyu ^{[2
]}

Su, Tse ^{[2
]}

机构：

[1] Ming Chi Univ Technol, Dept Elect Engn, New Taipei, Taiwan

[2] Oriental Inst Technol, Dept Commun Engn, New Taipei, Taiwan

来源：

2015 1ST INTERNATIONAL CONFERENCE ON INDUSTRIAL NETWORKS AND INTELLIGENT SYSTEMS (INISCOM) | 2015年

关键词：

differential evolution; elitist crossover; optimization; robust mutation; vector; PARAMETERS;

D O I：

10.4108/icst.iniscom.2015.258331

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, the robust mutation strategy for differential evolution (DE) is proposed for Enhancing its solution searching abilities. Also, the elitist crossover is involved to produce potential vectors. In the experiments, fifteen CEC 2005 test functions, which include uni-modal and multi-modal functions, are adopted for testing the proposed method and compare its performance with three DE variants. From the results, it can be observed that the proposed method performs better than other DE approaches on most test functions.

引用

页码：122 / 125

页数：4

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