Topology and migration policy of fine-grained parallel evolutionary algorithms for numerical optimization

Abstract

This paper proposes a complete binary tree topology and two efficient migration methods in fine-grained parallel evolutionary algorithms (FGPEAs) to solve constrained numerical optimization problems. The design of effective evolutionary algorithms (EAs) is to obtain a proper balance between exploration and exploitation. The balance can be controlled by the spread rate and the migration of the best individuals. A complete binary tree topology, which slows down the spread rate, is used for exploration to solve the heavily constrained problems. Two migration methods are also employed to prevent a superior individual from taking almost all the subpopulations and to facilitate the possibility of global search. One is the restriction of migration according to the migration times and the other is the modified individual migration by the mutation operators. The simulation results indicate that FGPEA using the proposed migration methods has better performance in constrained numerical optimization problems, and the FGPEA with the tree topology and the proposed migration methods shows good performance on heavily constrained numerical optimization problems.