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.

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.

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