Differential evolution for dynamic environments with unknown numbers of optima

Mathys Plessis () and Andries Engelbrecht ()

Journal of Global Optimization, 2013, vol. 55, issue 1, 73-99

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. Copyright Springer Science+Business Media, LLC. 2013

Keywords: Differential evolution; Dynamic environments; Competing populations; Moving peaks; Dynamic number of populations (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10898-012-9864-9

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