Review on applications of particle swarm optimization in solar energy systems

Abstract

Solar energy is one of the most important factors used in the development of the countries. Since it is a renewable energy source, it reduces the demand on the non-renewable energy sources such as fossil fuels, oil, natural gas, nuclear, and other sources. Therefore, many researchers have sought to improve the performance of solar energy systems via applying several metaheuristic methods such as particle swarm optimization (PSO) which simulates the behavior of the fish schools or bird flocks. PSO has been used in different applications including engineering, manufacturing, and medicine. The main process of the PSO is to determine the optimal position for each particle inside the population. This is performed through updating the position using the velocity of each particle and the shared information between the particles. The aim of this paper is to provide a review on the PSO’s applications to improve the performance of solar energy systems and to identify the research gap for future work. The literature review used in this study indicates that the PSO is a very promising method to enhance the performance of solar energy systems.