Improved And Application Based On Particle Swarm Algorithm

The particle swarm optimization was a kind of modern optimization method that was proposed by Eberhart and Kennedy through mimic natural biological community grazing. Later, Shi, who was the introduction of inertia weight to better control the convergence and, thus, the current standard PSO algorithm. Because the algorithm is simple, needs to adjust the few parameters, has been widely applied to function optimization, communication system design, electronic system design and economic management, etc.Particle swarm optimization is thinking an efficient optimization method by the domestic and overseas scholars, but oneself also exist some shortcomings, such as easily trapped into local optimal in the later and premature phenomenon. How to speed up the particle swarm algorithm convergence speed and avoid premature convergence is always the most researchers'focus of concern. In this paper, based on the standard particle swarm algorithm, some improvements were made. Introducing cloud theory, the particle swarm is divided into three populations. It is modified inertia weight using cloud method, at the same time modified the"social"and"cognitive"section, and the notion of mean was introduced, an improved cloud adaptive theory particle swarm optimization algorithm named CAMPSO is proposed; Considering the influence of inertia to the algorithm, a larger weights is helpful to improve the search ability of the global, while smaller weights will can enhance the local search capability. In view of this, based on position diversity and population diversity to revise the inertial weights of particle swarm optimization algorithm was proposed. Make the inertial weights with the position of the length and fitness value to change. Finally the improved method is used in solving engineering constraints in optimization. Numerical experiments show that the improved algorithm not only shows good performance in the higher dimensional nonlinear unconstrained optimization problem, but also shows its superiority in the engineering example of the constrained optimization problem also.Finally, this paper use particle swarm algorithm into sense range thought of glowworm algorithm is solving the engineering constraint optimization problems. Experimental results show that the improved algorithm is effective.