Hybrid Particle Swarm and Conjugate Gradient Optimization Algorithm

摘要

In this work we propose a different particle swarm optimization (PSO) algorithm that employs two key features of the conjugate gradient (CG) method. Namely, adaptive weight factor for each particle and iteration number (calculated as in the CG approach), and periodic restart. Experimental results for four well known test problems have showed the superiority of the new PSO-CG approach, compared with the classical PSO algorithm, in terms of convergence speed and quality of obtained solutions.