PSO is one of the Population-based Search Algorithm. PSO is a method that optimizes a scenario by iteratively and
trying to improve the solutions and it is a method for performing numerical optimization without precise information. PSO is
initialized with a population of random solutions called particles and here each individual is treated as a volume less particle in a ddimensional
search space. Differential Evolution is a population-based algorithm. DE is more likely to find a function's true global
optimum. Here we applied DE and PSO algorithm on clustering by using same parameters, population size to the efficiency of
these two algorithms and finding the best globest using PSO and DE algorithms
Keywords:Differential Evolution, Data Mining, Genetic Algorithms, Knowledge Data Discovery, Particle Swarm Optimization.