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

Wenchao Yi (), Yinzhi Zhou (), Liang Gao (), Xinyu Li () and Chunjiang Zhang ()
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Wenchao Yi: Huazhong University of Science and Technology
Yinzhi Zhou: Nanyang Technological University
Liang Gao: Huazhong University of Science and Technology
Xinyu Li: Huazhong University of Science and Technology
Chunjiang Zhang: Huazhong University of Science and Technology

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 7, No 10, 1559-1580

Abstract: 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. $$\varepsilon $$ ε constrained differential evolution ( $$\varepsilon $$ ε DE) algorithm is an effective method in dealing with the COPs. However, $$\varepsilon $$ ε 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 $$\varepsilon $$ ε feasible individuals driven local search called as $$\varepsilon $$ ε constrained differential evolution algorithm with a novel local search operator ( $$\varepsilon $$ ε DE-LS). The effectiveness of the proposed $$\varepsilon $$ ε 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.

Keywords: Constrained optimization problems; Constraint handling technique; $$\varepsilon $$ ε Constrained differential evolution; Local search operator; Engineering design optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-016-1199-9

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