Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

3 Scopus Citations
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Author(s)

  • Laizhong Cui
  • Zexuan Zhu
  • Qiuzhen Lin
  • Jianyong Chen
  • Nan Lu
  • Jian Lu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)122-143
Journal / PublicationInformation Sciences
Volume422
Early online date5 Sep 2017
StatePublished - Jan 2018

Abstract

The performance of differential evolution (DE) has been significantly influenced by trial vector generation strategies and control parameters. Various powerful trial vector generation strategies with adaptive parameter adjustment methods such that the population generation is guided by the elites have been proposed. This paper aims to strengthen the performance of DE by compositing these powerful trial vector generation strategies, making it possible to obtain the guidance of each individual from multiple elites concurrently and independently. In this manner, the deleterious behavior in which an individual is misguided by various local optimal solutions into unpromising areas could be restrained to a certain extent. An adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism (abbreviated as AMECoDEs) has been proposed in this paper. This algorithm concurrently employs two elites-guided trial vector generation strategies for each individual to generate two candidate solutions accordingly, and the best one is adopted to participate in the selection. Moreover, a novel shift mechanism is established to handle stagnation and premature convergence issues. AMECoDEs has been tested on the CEC2014 benchmark functions. Experimental results show that AMECoDEs outperforms various classic state-of-the-art DE variants and is better than or at least comparable to various recently proposed DE methods.

Research Area(s)

  • Adaptive control parameters, Composite differential evolution, Global numerical optimization, Multiple-elites-guided, Shift mechanism

Citation Format(s)

Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. / Cui, Laizhong; Li, Genghui; Zhu, Zexuan; Lin, Qiuzhen; Wong, Ka-Chun; Chen, Jianyong; Lu, Nan; Lu, Jian.

In: Information Sciences, Vol. 422, 01.2018, p. 122-143.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review