ABSTRACT
Particle Swarm Optimization (PSO) algorithm is an evolutionary algorithm based on population, by Kennedv and Fberhart is put forward in 1995. In PSO algorithm, a particle is belong to a solution of search space, and with each potential solution has a strict corresponding relation. With a group of random number as the initial value in a certain range, through iterative search to the optimal value. PSO algorithm has less parameters, advantages of simple structure, fast convergence rate. Due to its easy to understand, easy to implement, been successfully applied in many optimization problems. But the disadvantage is that there is late in the process of PSO algorithm in the optimization of slow convergence speed and easy to fall into local optimal solution of the problem.
