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A multi-population differential evolution with best-random mutation strategy for large-scale global optimization

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Abstract

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO.

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Acknowledgments

This work is supported by the NSFC (National Natural Science Foundation of China) project (grant number: 41861047, 41461078) and the Northwest Normal University young teachers’ scientific research capability upgrading program (NWNU-LKQN-17-6), The authors would also like to thank Professor Xiaodong Li for providing the source code of the CEC 2013 LSGO benchmark suite.

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  1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Yongjie Ma & Yulong Bai

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  1. Yongjie Ma
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  2. Yulong Bai
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Correspondence to Yongjie Ma.

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Ma, Y., Bai, Y. A multi-population differential evolution with best-random mutation strategy for large-scale global optimization. Appl Intell 50, 1510–1526 (2020). https://doi.org/10.1007/s10489-019-01613-2

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