Improved Particle Swarm Optimization based on Greedy and Adaptive Features

From the inception of Particle Swarm Optimization (PSO) technique, a lot of work has been done by researchers to enhance its efficiency in handling optimization problems. However, one of the general operations of the algorithm still remains obtaining global best solution from the personal best solutions of particles in a greedy manner. This is very common with many of the existing PSO variants. Though this method is promising in obtaining good solutions to optimization problems, it could make the technique susceptible to premature convergence in handling some multimodal optimization problems. In this paper, the basic PSO (Linear Decreasing Inertia Weight PSO algorithm) is used as case study. An adaptive feature is introduced into the algorithm to complement the greedy method towards enhancing its effectiveness in obtaining optimal solutions for optimization problems. The enhanced algorithm is labeled Greedy Adaptive PSO (GAPSO) and some typical continuous global optimization problems were used to validate its effectiveness through empirical studies in comparison to the basic PSO. Experimental results show that GAPSO is more efficient.