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External Hierarchical Archive Based Differential Evolution

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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

Evolutionary Algorithms (EAs) have become much popular in tackling kinds of complex optimization problems nowadays, and Differential Evolution (DE) is one of the most popular EAs for real-parameter numerical optimization problems. Here in this paper, we mainly focus on an external hierarchical archive based DE algorithm. The external hierarchical archive in the mutation strategy of DE algorithm can further improve the diversity of trial vectors and the depth information extracted from the hierarchical archive can achieve a better perception of the landscape of objective function, both of which consequently help this new DE variant secure an overall better optimization performance. Commonly used benchmark functions are employed here in verifying the overall performance and experiment results show that the new algorithm is competitive with other state-of-the-art DE variants.

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Authors and Affiliations

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

    Zhenyu Meng, Jeng-Shyang Pan & Xiaoqing Li

Authors
  1. Zhenyu Meng
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  2. Jeng-Shyang Pan
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  3. Xiaoqing Li
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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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Meng, Z., Pan, JS., Li, X. (2019). External Hierarchical Archive Based Differential Evolution. 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_8

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