A new variant of Particle Swarm Optimization (PSO) is developed to improve the performance of PSO, which has been widely used in various fields for optimization. The proposed PSO incorporates a space partitioning technique in grid method with PSO. In the searching process of the new algorithm, three position vectors are introduced to enhance the exploration of the particles in the population of PSO and hence helpful for a global optimization problem of interest. First, the proposed variant of PSO is verified by applying it to the seven benchmark functions and thereafter proved from the results as a robust one. Next, we applied the algorithm to the optimization design of the M-type spring used in the 3C equipments. With the maximum stress as the objective function of the designing product and the thrust as the constraint, we obtain from the computation the designing parameter set of the spring, which gives the designing spring a more uniform stress distribution and a reduction of the maximum stress by around 9.2% helpful to increase the lifetime of the initiated product.

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