A Review of Modified Particle Swarm Optimization Method

Abstract

Particle swarm optimization (PSO) is a nature-inspired population-based evolutionary and stochastic optimization method to solve optimization problems. In PSO, particles are communicating to each other using search directions. Nowadays, PSO is advanced search algorithm and metaheuristic technique which is used in different areas of applications. Some drawbacks of PSO algorithm are local optimum solution and low convergence rate. Many researchers have modified original PSO to remove the drawback for the improvement of performance and convergence-related problems of PSO algorithm. In this paper, we have presented a review on the modified particle swarm optimization algorithms in the direction of inertia weight, discrete particle swarm optimization (DPSO), parallel particle swarm optimization (PPSO), and perspective of convergence. An attempt is made to present a systematic framework for the researchers working in the area of PSO.