Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments

This paper reports three adaptations to DynDE, an approach to using Differential Evolution to solve dynamic optimization problems. The first approach, Competitive Population Evaluation (CPE), is a multi-population DE strategy aimed at locating optima faster in the dynamic environment. This approach is based on allowing populations to compete for function evaluations based on their performance. The second approach, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A third approach, consisting of a combination of CPE and RMC is investigated. The new strategies are empirically compared to DynDE using various problem sets. The empirical results show that the new approaches constitute an improvement over DynDE and other approaches in the literature.