A Particle Swarm Optimization Algorithm Based on Dynamic Adaptive and Chaotic Search

Abstract

Although the particle swarm optimization algorithm has the advantages of fast convergence, easy to use and strong versatility, the algorithm also has the defects of low search precision, poor local search ability and easy to fall into local optimal solution. Therefore, this paper proposes a particle swarm optimization algorithm based on dynamic adaptive and chaotic search to ensure the global search ability of the particle swarm while avoiding falling into the local optimal solution. The experimental results show that compared with the comparison algorithm, the DACSPSO has stronger global search ability, higher convergence precision, and can effectively avoid premature convergence.