Using stochastic dynamic step length particle swarm optimization to direct orbits of chaotic systems

Stochastic dynamic step length particle swarm optimization (SDSLPSO) is a new novel variant of particle swarm optimization that incorporating the dynamic step length for each particle in each iteration. This strategy simulates the phenomenon that each bird adjusts its velocity automatically in the process to finding the prey. In this paper, SD-SLPSO is employed to solve the directing orbits of chaotic systems, simulation results show this new variant increases the performance significantly when compared with the standard version of particle swarm optimization.