Differential Evolution: A Survey of the State-of-the-Art

TLDR

Differential evolution is a powerful stochastic real‑parameter optimization algorithm that, since 1995, has attracted extensive research and many variants due to its unique perturbation strategy that does not require a separate probability distribution. This paper reviews the core concepts of DE, surveys its major variants and applications to multiobjective, constrained, large‑scale, and uncertain optimization problems, and discusses related theoretical studies. DE variants generate offspring by perturbing current population members with scaled differences of randomly selected distinct members, eliminating the need for a separate probability distribution.

Abstract

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.