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Engineering design optimization using an improved local search based epsilon differential evolution algorithm

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Abstract

Many engineering problems can be categorized into constrained optimization problems (COPs). The engineering design optimization problem is very important in engineering industries. Because of the complexities of mathematical models, it is difficult to find a perfect method to solve all the COPs very well. \(\varepsilon \) constrained differential evolution (\(\varepsilon \)DE) algorithm is an effective method in dealing with the COPs. However, \(\varepsilon \)DE still cannot obtain more precise solutions. The interaction between feasible and infeasible individuals can be enhanced, and the feasible individuals can lead the population finding optimum around it. Hence, in this paper we propose a new algorithm based on \(\varepsilon \) feasible individuals driven local search called as \(\varepsilon \) constrained differential evolution algorithm with a novel local search operator (\(\varepsilon \)DE-LS). The effectiveness of the proposed \(\varepsilon \)DE-LS algorithm is tested. Furthermore, four real-world engineering design problems and a case study have been studied. Experimental results show that the proposed algorithm is a very effective method for the presented engineering design optimization problems.

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Acknowledgments

The authors would like to thank the editor and anonymous referees whose comments helped a lot in improving this paper. The authors would also like to thank Dr. Senbong Gee and Prof. Kaychen Tan for their constructive and insightful suggestions. This research work is supported by the Natural Science Foundation of China (NSFC) under Grant Nos. 51435009, 51421062 and 61232008.

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Authors and Affiliations

  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Wenchao Yi, Liang Gao, Xinyu Li & Chunjiang Zhang

  2. The School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Yinzhi Zhou

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  1. Wenchao Yi
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  2. Yinzhi Zhou
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Correspondence to Xinyu Li.

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Yi, W., Zhou, Y., Gao, L. et al. Engineering design optimization using an improved local search based epsilon differential evolution algorithm. J Intell Manuf 29, 1559–1580 (2018). https://doi.org/10.1007/s10845-016-1199-9

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