An Improved Particle Swarm Optimization Technique and its Application in Load Frequency Control

Particle swarm optimization (PSO) is a metaheuristic method and is widely used by several applications. Improving the performance of this optimization method can greatly enhance its practical utility. Several variations of the original PSO have been proposed by the researchers such as the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. Similarly, several methods have been reported to improve the performance of the PSO. In this paper, a simple segmentation method has been proposed for improving the performance of the PSO. In order to validate the approach, the improved PSO was applied for optimization of 14 different benchmarking cost functions and the results were compared with the original PSO algorithm. It is observed that using the proposed methodology, the cost functions were minimized to obtain a better optimal solution. This approach can also be used by the other variants of the PSO algorithms to achieve improved performance. Furthermore, the proposed PSO was applied for the practical optimization problem of load frequency control (LFC) of a two-area power system.