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A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems

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  • Zhao, Zhiwei
  • Yang, Jingming
  • Hu, Ziyu
  • Che, Haijun

Abstract

This paper presents a differential evolution (DE) algorithm, namely SLADE, with self-adaptive strategy and control parameters for unconstrained optimization problems. In SLADE, the population is initialized by symmetric Latin hypercube design (SLHD) to increase the diversity of the initial population. Moreover, the trial vector generation strategy assigned to each target individual is adaptively selected from the strategy candidate pool to match different stages of the evolution according to their previous successful experience. SLADE employs Cauchy distribution and normal distribution to update the control parameters CR and F to appropriate values during the evolutionary process. A large amount of simulation experiments and comparisons have been made by employing a set of 25 benchmark functions. Experimental results show that SLADE is better than, or at least comparable to, other classic or adaptive DE algorithms, and SLHD is effective for improving the performance of SLADE.

Suggested Citation

  • Zhao, Zhiwei & Yang, Jingming & Hu, Ziyu & Che, Haijun, 2016. "A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems," European Journal of Operational Research, Elsevier, vol. 250(1), pages 30-45.
    • Handle: RePEc:eee:ejores:v:250:y:2016:i:1:p:30-45
    DOI: 10.1016/j.ejor.2015.10.043
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    References listed on IDEAS

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    1. Mlakar, Miha & Petelin, Dejan & Tušar, Tea & Filipič, Bogdan, 2015. "GP-DEMO: Differential Evolution for Multiobjective Optimization based on Gaussian Process models," European Journal of Operational Research, Elsevier, vol. 243(2), pages 347-361.
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    5. Mahalec, Vladimir & Chen, Yingwu & Liu, Xiaolu & He, Renjie & Sun, Kai, 2015. "Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolutionAuthor-Name: Chen, Yingguo," European Journal of Operational Research, Elsevier, vol. 242(1), pages 10-20.
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    Cited by:

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    2. Chen, J.J. & Wu, Q.H. & Zhang, L.L. & Wu, P.Z., 2017. "Multi-objective mean–variance–skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties," European Journal of Operational Research, Elsevier, vol. 263(2), pages 719-732.
    3. Javier Cano & Cesar Alfaro & Javier Gomez & Abraham Duarte, 2022. "Out of the Niche: Using Direct Search Methods to Find Multiple Global Optima," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
    4. Tsafarakis, Stelios & Zervoudakis, Konstantinos & Andronikidis, Andreas & Altsitsiadis, Efthymios, 2020. "Fuzzy self-tuning differential evolution for optimal product line design," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1161-1169.
    5. Guohua Fang & Yuxue Guo & Xin Wen & Xiaomin Fu & Xiaohui Lei & Yu Tian & Ting Wang, 2018. "Multi-Objective Differential Evolution-Chaos Shuffled Frog Leaping Algorithm for Water Resources System Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3835-3852, September.
    6. Ma, Xuemin & Yang, Jingming & Sun, Hao & Hu, Ziyu & Wei, Lixin, 2021. "Feature information prediction algorithm for dynamic multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 295(3), pages 965-981.
    7. Gonggui Chen & Zhengmei Lu & Zhizhong Zhang, 2018. "Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems," Energies, MDPI, vol. 11(1), pages 1-27, January.

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