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Heterozygous differential evolution with Taguchi local search

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Abstract

Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to balance the exploration and exploitation is harder work to the researchers improving the performance of DE. Very often, we catch one and lose another. To overcome this problem, this paper presents a novel DE variant, called heterozygous DE with Taguchi local search (THDE), in which two new proposed methods (i.e., multiple schemes heterozygous evolution and Taguchi local search) are employed, with one as enhanced exploration ability and the other enhanced exploitation ability. The experimental studies have been conducted on 27 well-known test functions, including unimodal, multimodal and shifted test functions. Experimental results have verified the quality and effectiveness of THDE. Comparison with the state-of-the-art DE variants has proved that THDE is a type of new competitive algorithm.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61070008, No. 61364025), the Foundation of State Key Laboratory of Software Engineering (No. SKLSE2012-09-39), and the Science and Technology Foundation of Jiangxi Province, China (No. GJJ13729) as well.

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Authors and Affiliations

  1. State Key Lab of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Hu Peng & Zhijian Wu

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  1. Hu Peng
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  2. Zhijian Wu
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Correspondence to Zhijian Wu.

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Communicated by V. Loia.

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Peng, H., Wu, Z. Heterozygous differential evolution with Taguchi local search. Soft Comput 19, 3273–3291 (2015). https://doi.org/10.1007/s00500-014-1482-7

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