An Effective Particle Swarm Optimizer for Solving Complex Functions with High Dimensions
Kai Lei · 2006
For complex functions with high dimensions, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima. This paper proposes an efective particle swarm optimizer, which can automatically select a preferably inertia weight curve for the complex functions according to the fitness change ratio of swarm, and to balance global and local search ability, fasten convergence speed, avoid premature problem, and obtain global optimum. Experimental results on several benchmark complex functions with high dimensions show that the algorithm can rapidly converge at high quality solutions.