Particle swarm optimization with oscillating parameter strategy

A novel oscillating parameter strategy (OPS) is proposed for the particle swarm optimization algorithm to improve its performance after a predefined number of generations.To efficiently control the local search and convergence to the global optimum solution, the OPS method alternates exploration and exploitation many times during the whole optimization course.For implementing the alternative of exploration and exploitation, the inertia weight and acceleration coefficients are oscillated during the search process.The oscillating inertia weight and acceleration coefficients can enhance the global search in the early part and not fall into premature status.This also encourages the particle to converge toward the global optima at the end of the search.Empirical simulations showed that the OPS method outperformed all the methods considered in this investigation for most of the functions.