Parameter control for differential evolution by storage of successful values at an individual level

Abstract

Setting the parameters of differential evolution to appropriate values is critical for attaining satisfactory performance on a given problem (class). Therefore, parameter tuning is typically necessary, at least to some extent. However, even despite extensive tuning, the best possible performance may still not be achieved since different parameter values might be necessary during the different phases of the optimisation process. A solution to this issue is offered by parameter control schemes that properly adjust the parameters during the algorithm run. This paper presents a new self-adaptive parameter control scheme, which is based on the storage of a few previously successful values for each population member separately. These are then subsequently used to generate new and better-fitting parameter values with the aim of maintaining an effective search throughout the algorithm run. The highly competitive results obtained on challenging benchmark functions of different dimensionality suggest its viability and robustness. This is further corroborated by the promising results obtained on the problem of automatic radial basis function network design for classification needs.