The proposed Particle Swarm Optimization (PSO) variant uses a search space with a non-overlapping distinct search space for each particle in the population in the exploration of the optimum solution. What is normally done for a reduction in swarm size and achieving a much quicker response in PSO is to manually set the swarm size and other auxiliary constants through trial and error. An algorithm is proposed which assigns each particle to a unique non-verlapping finite search space and aggregates all particles position to form the solution at every functional evaluation. This assignment of the particles to a finite distinct search space is suitable for quick convergence with less iteration and less particle size comparatively. The theoretical basis is provided for the proposed algorithm and empirical studies are conducted to compare the proposed algorithm with other selected optimization algorithms on reference benchmark test functions.