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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Wenchao Yi & Yinzhi Zhou & Liang Gao & Xinyu Li & Chunjiang Zhang, 2018. "Engineering design optimization using an improved local search based epsilon differential evolution algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1559-1580, October.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-016-1199-9
    DOI: 10.1007/s10845-016-1199-9
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    References listed on IDEAS

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    1. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    2. Naber, Anulark & Kolisch, Rainer, 2014. "MIP models for resource-constrained project scheduling with flexible resource profiles," European Journal of Operational Research, Elsevier, vol. 239(2), pages 335-348.
    3. Han, Bin & Zhang, Wenjun & Lu, Xiwen & Lin, Yingzi, 2015. "On-line supply chain scheduling for single-machine and parallel-machine configurations with a single customer: Minimizing the makespan and delivery cost," European Journal of Operational Research, Elsevier, vol. 244(3), pages 704-714.
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    Cited by:

    1. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    2. Wenchao Yi & Liang Gao & Zhi Pei & Jiansha Lu & Yong Chen, 2021. "ε Constrained differential evolution using halfspace partition for optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 157-178, January.

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