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

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

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.

Citation

Segredo, E., Lalla-Ruiz, E., Hart, E., & Voß, S. (2020). A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution. Computers and Operations Research, 117, Article 104871. https://doi.org/10.1016/j.cor.2019.104871

• Journal Article Type: Article
• Acceptance Date: Dec 23, 2019
• Online Publication Date: Dec 24, 2019
• Publication Date: 2020-05
• Deposit Date: Jan 6, 2020
• Publicly Available Date: Jun 25, 2021
• Journal: Computers & Operations Research
• Print ISSN: 0305-0548
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 117
• Article Number: 104871
• DOI: https://doi.org/10.1016/j.cor.2019.104871
• Keywords: Differential evolution, global search, diversity management, exploration, exploitation, large-scale continuous optimization
• Public URL: http://researchrepository.napier.ac.uk/Output/2430238