Robust Differential Evolution for solving Numerical Optimization Problems

Chun Ling Lin; Sheng Ta Hsieh; Huang Lyu Wu; Tse Su

1st International Conference on Industrial Networks and Intelligent Systems

Research Article

Robust Differential Evolution for solving Numerical Optimization Problems

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@INPROCEEDINGS{10.4108/icst.iniscom.2015.258331,
    author={Chun Ling Lin and Sheng Ta Hsieh and Huang Lyu Wu and Tse Su},
    title={Robust Differential Evolution for solving Numerical Optimization Problems},
    proceedings={1st International Conference on Industrial Networks and Intelligent Systems},
    publisher={ICST},
    proceedings_a={INISCOM},
    year={2015},
    month={4},
    keywords={differential evolution elitist crossover optimization robust mutation vector},
    doi={10.4108/icst.iniscom.2015.258331}
}

Chun Ling Lin
Sheng Ta Hsieh
Huang Lyu Wu
Tse Su
Year: 2015
Robust Differential Evolution for solving Numerical Optimization Problems
INISCOM
ICST
DOI: 10.4108/icst.iniscom.2015.258331

Chun Ling Lin¹, Sheng Ta Hsieh²^,*, Huang Lyu Wu², Tse Su²

1: Department of Electrical Engineering, Ming Chi University of Technology
2: Department of Communication Engineering, Oriental Institute of Technology

*Contact email: fo013@mail.oit.edu.tw

Abstract

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.

Keywords: differential evolution elitist crossover optimization robust mutation vector

Published: 2015-04-09
Publisher: ICST

: http://dx.doi.org/10.4108/icst.iniscom.2015.258331