This paper evaluates the performance of three Particle Swarm Optimization (PSO) algorithms, namely Attraction-Repulsion based PSO (ATREPSO), Quadratic Interpolation based PSO (QIPSO) and Gaussian Mutation based PSO (GMPSO). Whereas all the algorithms are guided by the diversity of the population to search the global optimal solution of a given optimization problem, GMPSO uses the concept of mutation and QIPSO uses the reproduction operator to generate a new member of the swarm. We tested the variants of PSO on ten standard benchmark functions and compared the results with classical PSO algorithm. Also, the performance of all algorithms is tested on two engineering design problems. The numerical results show that all the algorithms outperform the classical Particle Swarm Optimization by a remarkable difference.