A novel similarity-based mutant vector generation strategy for differential evolution

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

Abstract

The mutant vector generation strategy is an essential component of Differential Evolution (DE), introduced to promote diversity, resulting in exploration of novel areas of the search space. However, it is also responsible for promoting intensification, to improve those solutions located in promising regions. In this paper we introduce a novel similarity-based mutant vector generation strategy for DE, with the goal of inducing a suitable balance between exploration and exploitation, adapting its behaviour depending on the current state of the search. In order to achieve this balance, the strategy considers similarities among individuals in terms of their Euclidean distance in the decision space. A variant of DE incorporating the novel mutant vector generation strategy is compared to well-known explorative and exploitative adaptive DE variants. An experimental evaluation performed on a well-known suite of large-scale continuous problems shows that the new DE algorithm that makes use of the similarity-based approach provides better performance in comparison to the explorative and exploitative DE variants for a wide range of the problems tested, demonstrating the ability of the new component to properly balance exploration and exploitation.

Citation

Segredo, E., Lalla-Ruiz, E., & Hart, E. (2018, July). A novel similarity-based mutant vector generation strategy for differential evolution. Presented at The Genetic and Evolutionary Computation Conference 2018 (GECCO 2018), Kyoto, Japan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The Genetic and Evolutionary Computation Conference 2018 (GECCO 2018)
• Start Date: Jul 15, 2018
• End Date: Jul 19, 2018
• Acceptance Date: Mar 24, 2018
• Online Publication Date: Jul 2, 2018
• Publication Date: 2018
• Deposit Date: Apr 3, 2018
• Publicly Available Date: Jan 3, 2019
• Journal: Proceedings of the Genetic and Evolutionary Computation Conference
• Publisher: Association for Computing Machinery (ACM)
• Book Title: Proceedings of the Genetic and Evolutionary Computation Conference 2018
• ISBN: 9781450356183
• DOI: https://doi.org/10.1145/3205455.3205628
• Keywords: Global optimization, Differential evolution, Similarity, Diversity, Large-scale optimization
• Public URL: http://researchrepository.napier.ac.uk/Output/1140654
• Contract Date: Apr 3, 2018