An adaptive coevolutionary differential evolution algorithm for large-scale optimization

  • Authors:

  • Zhenyu Yang;Jingqiao Zhang;Ke Tang;Xin Yao;Arthur C. Sanderson

  • Affiliations:

  • Nature Inspired Computation and Applications Laboratory, The Dept. of Computer Science and Technology, Univ. of Science and Technology of China, Hefei, Anhui, China;Center for Automation Technologies and Systems, Rensselaer Polytechnic Institute, Troy, NY;Nature Inspired Computation and Applications Laboratory, The Dept. of Computer Science and Technology, Univ. of Science and Technology of China, Hefei, Anhui, China;Nature Inspired Computation and Appl. Laboratory, The Dept. of Comp. Sci. and Techn., Univ. of Sci. and Techn. of China, Hefei, Anhui, China and CERCIA, the School of Comp. Sci., Univ. of Birmingh ...;Center for Automation Technologies and Systems, Rensselaer Polytechnic Institute, Troy, NY

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weighting based cooperative coevolution algorithm, DECC-G [1], which uses random grouping strategy to divide the objective vector into subcomponents, and solve each of them in a cyclical fashion. The adaptive weighting mechanism is used to adjust all the subcomponents together at the end of each cycle. In the new JACC-G algorithm: (1) A most recent and efficient Differential Evolution (DE) variant, JADE [2], is employed as the subcomponent optimizer to seek for a better performance; (2) The adaptive weighting is time-consuming and expected to work only in the first few cycles, so a detection module is added to prevent applying it arbitrarily; (3) JADE is also used to optimize the weight vector in adaptive weighting process instead of using a basic DE in previous DECC-G. The efficacy of the proposed JACC-G algorithm is evaluated on two sets of widely used benchmark functions up to 1000 dimensions.