Particle Swarm Optimization And Its Application

In view of the complexity of the optimization problem, and increasing calculationof solving optimization problem of the traditional algorithm, it makes people to put thenew and higher requirements for the method of solving the problems, and efficientoptimization technology and the requirement of intelligent computation are particularlyurgent.Particle swarm optimization is a new intelligence algorithm, which is proposedaccording to simulation of bird foraging behavior. The algorithm has the advantages ofsimple principle and structure, fewer parameters, and it doesn’t rely too much on theinformation of the solving problems, and it has fast convergence has strong ability inglobal search, so it has strong commonality. For the many deficiencies of the algorithm,many scholars proposed a lot of improved algorithms, and applied it to a lot of practicalproblems.This paper concentrates on the principle of PSO, and it takes further study onimprovement and application of the PSO. For solving the constrained optimizationproblem in application of the PSO, combining a multiplier method which deals withconstraint problems with improved particle swarm optimization algorithm, a newmethod is proposed for solving non-linear constraint problems, which is based on theparticle swarm optimization. The new algorithm takes advantage of the particle swarmoptimization algorithm and the multiplier method, for the non-available particleappearing in the iterative process, using the multiplier method to produce feasibleparticle, and then search its optimal value by improved particle swarm optimization.Thus it can not only reduce the probability of falling into local minimum, but also canimprove the search accuracy. And the numerical tests show that the proposed newalgorithm has the characteristics of validity and searching for more precise particle andbetter robustness.