A Multi-swarm Artificial Bee Colony Algorithm for Dynamic Optimization Problems
Artificial Bee Colony (ABC) algorithm has been applied to many scientific and engineering problems for its efficiency. However, the original ABC technique cannot be used in dynamic environments directly. This paper proposes a multiswarm ABC algorithm, or MABC, that has a similar framework as the original ABC but uses environment detecting technique to track the moving of the optimum of dynamic problems. Experiments have been carried out by comparing MABC with original ABC on moving peaks benchmark (MPB). Results show that the proposed MABC performs better in terms of the offline error, convergence speed, and robustness.