On Modified Strategy Based on the Model of Particle Swarm Optimization

Aiming at the disadvantages of premature convergence and slow later evolution in the basic particle swarm optimization algorithm, this paper studies the influence of three parameter omega, c1 and c2 on the convergence and divergence and search speed on the basic of PSO´s model, then puts forward the strategy of dynamically adjusting the three parameter setting . Modified algorithm makes particle swarm achieve different search performance at different evolution stage, then increases the diversity of the particle swarm, improves the convergence speed of the particle, and coordinates the equilibrium between local and global convergence. As a result, these modifications make the algorithm very quickly and accurately search global optimal solution. Finally, modified algorithm is proved effective by the simulation example of the typical optimization problem.