The improvised Particle Swarm Optimization (PSO) Algorithm offers better search efficiency than conventional PSO algorithm. It provides an efficient technique to obtain the best optimized result in the search space. This algorithm ensures a faster rate of convergence to the desired solution whose precision can be preset by the user. The inertia parameter is varied linearly with iteration number, which results in more accurate solution for unimodal functions. The control over the precision value acts as a trade-off between the convergence time and precision of the desired solution, and it can be viewed as a performance parameter. Swarm convergence is followed by a mutation process, which further improves the obtained result by enhancing the local search ability of some particles. The results show that the solution with predefined precision level can be obtained with the minimum number of iterations.