This paper investigates optimization in dynamic environments where the numbers
of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic
Population DifferentialEvolution (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.