The Synthesis And Improvement Of PSO

Particle Swarm Optimization (PSO) algorithm is one of the most powerful methods for solving unconstrained and constrained global optimization problems. It was originally introduced by Kennedy and Eberhart in1995. PSO is simple in principles, few in parameters, fast in convergence rate and so on. It has been widely applied in function optimization. But neither the theoretical analysis nor applications is completely mature, so there are many questions to be researched. Since PSO is easy to be trapped into local minima in optimizing higher dimensional optimizing problem, chaos algorithm is incorporated into the PSO in this paper and a new particle swarm optimization algorithm based on Hénon map is proposed. The proposed algorithm has not only the advantage of original particle swarm optimization, but also the fast convergence and high computational precision of chaos optimization algorithm.This paper looks back the background of swarm intelligence theory, summarizes the theory and development of three swarm intelligence algorithms, then introduces the basic theory, math description, parameters and flowchart of PSO. The effect of adaptive inertia weight is also discussed. Based on the analysis of convergence tendencies and confinements of the particle swarm, three methods are presented to improve the performance of the algorithm: increasing the convergence velocity, getting rid of the stagnation, sustaining the diversity of the swarm. Meanwhile something important but easily neglected in the PSO and some other algorithms are also introduced.Chaos optimization method as a new optimization technology in recent years is usually based on Logistic or Tent map to produce chaos sequence and we always use the properties of periodicity and randomness of the chaos sequence for local searching. However, the probability density function of chaotic sequence of Logistic map is a Chebyshev type function, which may affect the global searching ability and computational efficiency of chaos optimization algorithm severely when optimal point is located in the middle part of interval [0, 1].On the other hand, the Tent map based algorithms are easy to run into small periodic cycle. In order to overcome the demerits mentioned above, a Hénon map based PSO optimization algorithm is proposed(CHPSO). It can not only overcome the disadvantage of easily getting into the local extremes in the later evolution period, but also keep the rapidity of the previous period.Finally, the basic particle swarm optimization algorithm is compared with CHPSO. The experiment results demonstrate that the new algorithm proposed is better than the basic particle swarm optimization algorithm in the aspects of convergence and stability.