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

• Published in: European journal of operational research Vol. 218; no. 1; pp. 7 - 20
• Main Authors: du Plessis, Mathys C., Engelbrecht, Andries P.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2012; Elsevier; Elsevier Sequoia S.A
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Comparative analysis; Computer science; control theory; systems; Differential evolution; Dynamic environments; Evolutionary computation; Exact sciences and technology; Mathematical problems; Operations research; Optimization; Optimization algorithms; Studies; Theoretical computing; Differential evolution; Optimization; Evolutionary computation; Dynamic environments; Function evaluation; Evolutionary algorithm; Competitiveness; Empirical method; Problem solving; Dynamic programming; Competitive algorithms; Mathematical programming
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2011.08.031

► DynDE is a differential evolution-based algorithm for solving dynamic optimization problems. ► Competitive Population Evaluation (CPE) adapts DynDE to locate optima earlier. ► CPE is based on allowing populations to compete for function evaluations based on their performance. ► Reinitialization Midpoint Check (RMC) adapts DynDE to better maintain populations on different peaks in the search space. ► The empirical results show that the new approaches constitute an improvement over DynDE. This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE, which is shown to yield improved results.