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The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as quadric, griewanks, rastrigin, ackley, and weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO.