Domain Learning Particle Swarm Optimization With a Hybrid Mutation Strategy

Abstract

When traditional particle swarm optimization algorithms deal with highly complex, ultra-high-dimensional problems, traditional particle learning strategies can only provide little help. In this paper, a particle swarm optimization algorithm with a hybrid variation domain dimension learning strategy is proposed, which uses the domain dimension average of the current particle dimension to generate guiding particles. At the same time, an improved inertia weight is also used, which effectively avoids the algorithm from easily falling into local optimum. To verify the strong competitiveness of the algorithm, the algorithm is tested on nineteen benchmark functions and compared with several well-known particle swarm algorithms. The experimental results show that the algorithm proposed in this paper has a significant effect on unimodal functions, and has a better effect on multimodal functions. Guided particles, improved inertia weight and mutation strategy can effectively balance local search and global search, and can better converge to the global optimal solution.