Particle swarm optimization algorithm: review and applications

Particle swarm optimization (PSO) is a heuristic global optimization technique and an optimization algorithm that is swarm intelligence-based. It is based on studies into the movement of bird flocks. Individual birds share information about their position, speed, and fitness while searching the food source, and the flock's behavior is affected to enhance the likelihood of migration to high-fitness areas. This paper surveys the published papers in PSO algorithms. Twenty research papers are analyzed and classified according to the implementation area used by the PSO algorithm (neural networks, feature selection, and data clustering). The main procedure of the PSO algorithm is presented. Future researchers can use the collected data in this survey as baseline information on the PSO and PSO's applications.