Enhancing distributed differential evolution with a space-driven topology

Abstract

Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing population diversity as well as algorithmic performance. Sub-populations in DDE share their elite individuals with neighborhood through a predefined migration topology. However, the construction of traditional migration topologies does not consider the position information of sub-populations in the search space. The position information is helpful in controlling the degree of diversity between the sub-populations and their migrated individuals. A proper degree of diversity could promote the balance between exploration and exploitation for DDE algorithms. To achieve this target, a dynamic space-driven migration topology is proposed in this paper. The proposed topology is constructed and updated according to the distances between sub-populations. Based on this proposed topology, some sub-populations receive diverse individuals from neighborhood far away while others communicate with neighborhood nearby. Numerical experiments have been performed on 13 diverse test functions. Results verify the advantage of DDE with the proposed migration topology compared to those with classic topologies.