A Particle Swarm Algorithm Based on Stochastic Evolutionary Dynamics
Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on stochastic evolutionary dynamics (SED-PSO). The stochastic evolutionary dynamics is used to speed up the researching process of the particles because stochastic factor plays a very important role in the researching process of optimal algorithm. Each particle in the swarm is also associated with a process of reproduction. We use a stochastic process with frequency dependent fitness to deal with the reproduction process. Experiments results show that SED-PSO algorithm has great performance of convergence property over traditional PSO in terms of iteration with only a slight precision dropdown.