Hybrid particle swarm optimization algorithm
QI Ming-jun · Computer Engineering and Applications Journal · 2012
Using Particle Swarm Optimization(PSO)to handle complex functions with high-dimension has the problems of low convergence speed and premature convergence. This paper proposes a hybrid particle swarm optimization. It adopts prematurity judge mechanism by the variance of the population’s fitness and puts gene conversion and mutation operator into algorithm. It constructs a new individual and individual gene fitness function, and will adapt to the worst gene mutation value. To reduce the computation of the proposed algorithm, it uses quantum dissipative particle swarm algorithm structure. Experimental results show that compared with particle swarm algorithm which has only one fitness value, it has faster convergence rate. Especially the hybrid particle swarm optimization is of strong ability to avoid being trapped in local minima, and performances are fairly superior to single method.