Differential evolution with improved mutation strategy

Abstract

Differential evolution is a powerful evolution algorithm for optimization of real valued and multimodal functions. To accelerate its convergence rate and enhance its performance, this paper introduces a top-p-best trigonometric mutation strategy and a self-adaptation method for controlling the crossover rate (CR). The performance of the proposed algorithm is investigated on a comprehensive set of 13 benchmark functions. Numerical results and statistical analysis show that the proposed algorithm boosts the convergence rate yet maintaining the robustness of the DE algorithm. © 2011 Springer-Verlag.