An Improved Chaos-Particle Swarm Optimization Algorithm

To deal with the problems of premature and local convergence of conventional simple particle swarm optimization algorithm (SPSO), an improved chaosparticle swarm optimization algorithm (CPSO) is proposed in this paper. By means of ergodicity and randomicity of chaos algorithm, the initial population is generated by using appropriately chaotic mapping, so that these particles can be scattered uniformly over the solution space. When SPSO gets into the local convergence, CPSO can start chaotic researching in the solution space, and partly replace the preparticles so as to make the whole population jump out of the local minima. Experiments on seven benchmark functions show that CPSO outperforms SPSO in searching precision, convergence rate and stability.