A highly efficient particle swarm optimizer for super high-dimensional complex functions optimization

Because of the complexity of super high-dimensional complex functions with the large numbers of global and local optima, the general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima. In this paper, a highly efficient particle swarm optimizer is proposed, which employ the adaptive strategy of inertia factor, global optimum, search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the new algorithm and compare it with other published methods on several super high dimensional complex functions, the experimental results showed clearly the revised algorithm can rapidly converge at high quality solutions.