The Particle Swarm Optimization (PSO) Algorithm is one of swarm intelligence optimization algorithms. Usually the population's values of PSO algorithm are random which leads to random distribution of search quality and search velocity. This paper presents PSO based on uniform design (UD). UD is widely used in various applications and introduced to generate an initial population, in which the population members are scattered uniformly over the search space. In evolution, UD is also introduced to replace some worse individuals. Based on abovementioned these technologies a Particle Swarm Optimization Algorithm based on Uniform Design (PSO-UD) algorithm is proposed. At last, the performance of PSO-UD algorithm is tested and compared. Tests show that the PSO-UD algorithm faster than standard PSO algorithm with random populations.
A Multi-objective problems occurs wherever optimal solution necessary to be taken in the presence of tradeoffs between more than one conflicting objectives. Usually the population's values of MOPSO algorithm are random which leads to random search quality. Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design (MOPSO-UD), is proposed to enhance the accuracy of the particles convergence and keep the versatility of the Pareto optimal solutions and used the Uniform design to resolve the randomize search problem of the original MOPSO algorithm also the execution time of MOPSO-UD is faster compared with multi-objective particle swarm optimization algorithm (MOPSO).
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