Since the introduction of Particle Swarm optimization by Dr. Eberhart and Dr. Kennedy, there have been many variations of the algorithm proposed by many researchers and various applications presented using the algorithm. In this paper, we applied variants of Particle swarm optimization on various benchmark functions in multiple dimensions, using the computational procedure to find the optimal solutions for those functions. We ran the variants of the algorithm 51 times on each of the 17-benchmark functions and computed the average, variance and standard deviation for 10, 30, and 50 dimensions. Using the results, we found the suitable variants of the algorithm for the benchmark functions by considering the minimum optimal solution produced by each variant.