Improved Multi-Population Differential Evolution for Large-Scale Global Optimization

Authors

  • Yongjie Ma College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu
  • Lin Zhu College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu
  • Yulong Bai College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu

Keywords:

Differential evolution, large-scale global optimization, multiple populations, best-and-current mutation strategy, random migration strategy

Abstract

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, however, it is challenged to deal with high-dimensional problems. In this paper, we propose an improved multi-population differential evolution with best-and-current mutation strategy (mDE-bcM). The population is divided into three subpopulations based on the fitness values, each of subpopulations uses different mutation strategy. After crossover, mutation and selection, all subpopulations are updated based on the new fitness values of their individuals. An improved mutation strategy is proposed, which uses a new approach to generate base vector that is composed of the best individual and current individual. The performance of mDE-bcM is evaluated on a set of 19 large-scale continuous optimization problems, a comparative study is carried out with other state-of-the-art optimization techniques. The results show that mDE-bcM has a competitive performance compared to the contestant algorithms and better efficiency for large-scale optimization problems.

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Published

2020-12-16

How to Cite

Ma, Y., Zhu, L., & Bai, Y. (2020). Improved Multi-Population Differential Evolution for Large-Scale Global Optimization. COMPUTING AND INFORMATICS, 39(3), 481–509. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/2020_3_481