Abstract
Particle Swarm Optimization (PSO) is one of the evolutionary computation techniques based on the social behaviors of birds flocking or fish schooling, biologically inspired computational search and optimization method. Since first introduced by Kennedy and Eberhart [7] in 1995, several variants of the original PSO have been developed to improve speed of convergence, improve the quality of solutions found, avoid getting trapped in the local optima and so on. This paper is focused on performing a comparison of different PSO variants such as full model, only cognitive, only social, weight inertia, and constriction factor. We are using a set of 4 mathematical functions to validate our approach. These functions are widely used in this field of study.
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