New Hybird Particle Swarm Optimization Algorithm

To avoid trapping to local minima and improve the searching performance of simple particle swarm optimization(PSO) algorithm,a new hybird particle swarm optimization based on the simulated annealing(SA) and chaos is proposed.At the beginning stage,the location of the particle is initialized by chaos and then evaluates the fitness of particle by SA.During the running time,according to the variance of the population's fitness,the chaotic update of the particle is performed adaptively.At last the experimental results using the testing functions show that it is prior to standard PSO in search time and precision,and avoids the premature convergence.