Journal of Computer Applications (计算机应用) 2013/33:12 PP.3372-3374
In order to further improve the efficiency of the population diversity in the implementation process of the Particle Swarm Optimization (PSO), an Adaptive PSO (APSO) algorithm based on diversity feedback was proposed. APSO adopted a new population diversity evaluation strategy which enabled the automatic control of the inertia weight with population diversity in the search process to balance exploration and the exploitation's process. In addition, an elite learning strategy was used in the globally best particle to jump out of local optimal solution. It not only ensured the convergence rate of the algorithm, but also adaptively adjusted the search direction to improve the accuracy of solutions. The simulation results on a set of typical test functions verify the validity of APSO.