A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution

Eduardo Segredo, Eduardo Lalla-Ruiz*, Emma Hart, Stefan Voß

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (DE) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on current progress. The method—Similarity-based Neighbourhood Search (SNS)—uses information derived from measuring Euclidean distances among solutions in the decision space to adaptively influence the choice of neighbours to be used in creating a new solution. SNS is integrated into explorative and exploitative variants of JADE, one of the most frequently used adaptive DE approaches. Furthermore, SHADE, which is another state-of-the-art adaptive DE variant, is also considered to assess the performance of the novel SNS. A thorough experimental evaluation is conducted using a well-known set of large-scale continuous problems, revealing that incorporating SNS allows the performance of both explorative and exploitative variants of DE to be significantly improved for a wide range of the test-cases considered. The method is also shown to outperform variants of DE that are hybridised with a recently proposed global search procedure, designed to speed up the convergence of that algorithm.

Original languageEnglish
Article number104871
JournalComputers and Operations Research
Volume117
Early online date24 Dec 2019
DOIs
Publication statusPublished - May 2020

Keywords

  • Differential evolution
  • Diversity management
  • Exploitation
  • Exploration
  • Global search
  • Large-scale continuous optimization

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