Multi-swarm Optimization with Chaotic Mapping for Dynamic Optimization Problems

In real-world applications, many optimization problems are dynamic, therefore the goal of optimization algorithms is not only to obtain the optimal solution, but also to have strong adaptive capability to the environment changes and track the trajectory of the optimal solution as closely as possible. In this paper, a new multi-swarm optimization algorithm with chaotic mapping strategy based on particle swarm optimization (PSO) is proposed. The proposed algorithm adopts an improved multi-swarm approach and employs PSO as global and local search method. A modified chaotic mapping mechanism is presented to overcome the challenge of diversity loss. The Moving Peaks Benchmark is utilized to evaluate the performance of the proposed algorithm and experimental results have been compared with other algorithms. The results show that the proposed algorithm has good performance and outperforms others on most of the test cases.