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A Multi-population QUasi-Affine TRansformation Evolution Algorithm for Global Optimization

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Genetic and Evolutionary Computing (ICGEC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 834))

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Abstract

In this paper, we propose a new Multi-Population QUasi-Affine TRansformation Evolution (MP-QUATRE) algorithm for global optimization. The proposed MP-QUATRE algorithm divides the population into three sub-populations with a sort strategy to maintain population diversities, and each sub-population adopts a different mutation scheme to make a good balance between exploration and exploitation capability. In the experiments, we compare the proposed algorithm with DE algorithm and QUATRE algorithm on CEC2013 test suite for real-parameter optimization. The experimental results indicate that the proposed MP-QUATRE algorithm has a better performance than the competing algorithms.

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Author information

Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Nengxian Liu, Jeng-Shyang Pan, Xiangwen Liao & Guolong Chen

  2. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

Authors
  1. Nengxian Liu
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  2. Jeng-Shyang Pan
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  3. Xiangwen Liao
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  4. Guolong Chen
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Corresponding author

Correspondence to Jeng-Shyang Pan .

Editor information

Editors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, China

    Jerry Chun-Wei Lin

  3. Changzhou College of Information Technology, Changzhou, Jiangsu, China

    Bixia Sui

  4. College of Smart Living and Management, Tajen University, Pingtung, Taiwan

    Shih-Pang Tseng

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Liu, N., Pan, JS., Liao, X., Chen, G. (2019). A Multi-population QUasi-Affine TRansformation Evolution Algorithm for Global Optimization. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_3

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