An Effective Hybrid Optimization Algorithm Based on Self-Adaptive Particle Swarm Optimization Algorithm and Artificial Immune Clone Algorithm

Abstract

To balance the exploration and exploitation abilities of particle swarm optimization (PSO), self-adaptive inertia weight factor is introduced in PSO. To improve the ability of each algorithm to escape from a local optimum, a hybrid optimization algorithm (PAHA) based on self-adaptive PSO and artificial immune clone algorithm (AICA) is developed. Simulation results have shown that PAHA is effective and efficient for the optimization problems.