Journal of South China University of Technology (Natural Science Edition) ›› 2017, Vol. 45 ›› Issue (3): 97-103,116.doi: 10.3969/j.issn.1000-565X.2017.03.014

• Computer Science & Technology • Previous Articles     Next Articles

A Hybrid Differential Evolution Algorithm with Multiple Search Strategies for Large-Scale Optimization

LUO Jia-xiang NI Xiao-ye HU Yue-ming   

  1. School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2016-06-30 Revised:2016-10-25 Online:2017-03-25 Published:2017-02-02
  • Contact: 罗家祥( 1979-) ，女，博士，副教授，主要从事电子制造工业以及实际生产过程中的生产与优化调度、智能优化方法研究． E-mail:luojx@scut.edu.cn
  • About author:罗家祥( 1979-) ，女，博士，副教授，主要从事电子制造工业以及实际生产过程中的生产与优化调度、智能优化方法研究．
  • Supported by:
    Supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China( 2014ZX02503-3) and the National Natural Science Foundation of China( 61573146)

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Abstract:

Large-scale optimization problem with multiple peaks and high dimension is a hot topic in current optimization research field.By using the co-evolutionary algorithm as the framework,this paper proposes a hybrid differential evolution ( DE) algorithm with multiple search strategies to solve the large-scale optimization problem.In this algorithm,firstly,based on the thought of decomposition,a self-adaptive DE operator is applied to a local optimization of sub-problems.Then,a random search mechanism based on simulated annealing is introduced to improve the algorithm s global search ability,and a local search chain is combined to search the solution space deeply.Finally,a set of benchmark functions is employed to evaluate the proposed algorithm.The results show that the algorithm is prior to the existing ones because it helps obtain better average value and optimized solution.

Key words: large-scale optimization, co-evolutionary algorithm, simulated annealing, differential evolution, local search

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