A Improved Basic Particle Swarm Optimization Algorithm

Since the 1980s,intelligent optimization algorithm such as neural network,GA has been developed through the simulation of nature and social process and it presents a new approach for optimization methods. Particle swarm optimization(PSO)is inspired by social behavior of bird flocking or fish schooling.It is a population-based,self-adaptive search optimization technique.PSO is simple in concept,few in parameters,and easy in implementation,As a kind of intelligent algorithm,it can be used to solve various optimization problems and shows great potential in practice.Now,it has been widely appliedin many other areas,such as function optimization,artificial neural network and fuzzy system control.PSO is similar to GA in that the system is initialized with a population of random solutions,and the potential solutions,called particles,are then "flown" through the problem space.Each particle keeps track of its coordinates in the problem space which are associated with the best solutions it has achieved so far and obtained so far by any particles in the population.The attractive character of PSO concept is quite simple and easy to implement.This paper makes a systematic dissertation on the basic principle of PSO Algorithm,Parameter Options,Boundary Condition,Social Behavior Analysis,Hybrid Algorithm and its use,and the current state and development of national and international research,It also briefly introduces the Chaos Theory,Stimulated;Annealing Algorithm and Guotao Algorithm.Through the exquisite research of PSO Algorithm,it introduces Chaos Theory at the beginning of the algorithm to optimize the original groups;it uses the strategies of producing the niche and stimulated annealing by chaos variation to induct the particles renovation in the middle;it embeds Guotao Algorithm to abtain many optimization in the end.The result of the experiment on developed algorithm with base function shows that the new algorithm not only possesses better convergent precision and faster convergent speed,but also makes more effective overall search,especially while solving the problems of multi-peak functions opotimization.