Improved Particle Swarm Algorithm Based on Arnold Map
Junhui Wang · 2010
Particle swarm optimization is one of the heuristic global optimization algorithms,which has attracted vast attentions of researchers.Based on the analysis of the current improved algorithm,one improved algorithm was proposed in this paper,which employs Arnold chaotic map and one dimension disturbance term to improve the particle swarm algorithm.In the proposed algorithm,the researching ability of global optimization of particle swarm is enhanced through the improving of single particle.The simulation results show that the algorithm can keep the population's diversity better,and the performance of the particle swarm is increased notably.