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External Hierarchical Archive Based Differential Evolution

  • Zhenyu Meng
  • Jeng-Shyang PanEmail author
  • Xiaoqing Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)

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.

Keywords

Depth information Differential evolution Evolutionary algorithm Hierarchical archive 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Fujian Key Lab of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina

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