Differential evolution for dynamic environments with unknown numbers of optima

Authors:
Mathys C. Plessis;Andries P. Engelbrecht
Affiliations:
Department of Computing Sciences, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa;Department of Computer Science, University of Pretoria, Pretoria, South Africa
Venue:
Journal of Global Optimization
Year:
2013

Citing 19
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Abstract

This paper investigates optimization in dynamic environments where the numbers of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic Population Differential Evolution (DynPopDE), which is specifically designed for these problems. DynPopDE is a Differential Evolution based multi-population algorithm that dynamically spawns and removes populations as required. The new algorithm is evaluated on an extension of the Moving Peaks Benchmark. Comparisons with other state-of-the-art algorithms indicate that DynPopDE is an effective approach to use when the number of optima in a dynamic problem space is unknown or changing over time.