Adaptive multi-swarm in dynamic environments

Abstract

Abstract Multi-population is a promising approach to optimization in dynamic environments. To appropriately distribute multiple populations to distinct areas of the search space and refine the best solution found by each population, an adaptive multi-swarm framework for dynamic optimization problems is proposed, in which several adaptations of multi-population approaches are developed for a better exploration/exploitation tradeoff. As the first intention, a basic adaptation is the combination of a group of active swarms and a group of inactive swarms. The group of active swarms are devoted to exploring new areas of the search space, and the group of inactive swarms are devoted to preserving useful experiences. One kind of swarm can be transformed into another. An active swarm becomes inactive after it converges. An inactive swarm will become active and search for new optima again when an environmental change occurs. For the second intention, another basic adaptation is the application of a local search to the best individual of a stagnated swarm. The experimental results on various moving peaks benchmarks show that the proposed framework is competitive with other state-of-the-art methods and more effective for dynamic environments under many peaks, severe changes, and high dimensionalities.