ABSTRACT
Particle swarm optimization (PSO) is the pioneer of swarm intelligence algorithms, which utilizes collective effort for achieving better convergence for optimization. It was introduced by J. Kennedy and R. Eberhart in the year 1995 [1,2]. The history of the development of PSO is quite interesting as the two founders came from diverse backgrounds and they had been cooperating for sociopsychological simulation purposes and the outcome is one of the nest algorithms for optimization and problem-solving. The PSO algorithm mainly works on the basis of two factors, one is the collective exploration and the second behavior is the social guidance-based inuence of the coparticles for decision-making of the other particles.
