An Elitist Adaptive Particle Swarm Optimization Algorithm for Numerical Optimization

摘要

The particle swarm optimization (PSO) algorithm is a new evolutionary computation method. In this paper a new adaptive method is used in PSO algorithm for numerical optimization. It can adjust parameters automatically in optimization process to find the global optimum. In order to reduce the calculation time, the elitist strategy is also used in PSO algorithm. The motivation behind this concept is to well balance the exploration and exploitation capability for attaining better convergence to the optimum. Numerical results of the proposed approach, compared with other algorithms, show that the E-A-PSO algorithm is more efficient in searching global optimization solution.