Particle swarm optimization algorithm based on fixed distribution

In the process of searching for the optimal solution, particle swarm optimization algorithm falls into the local optimal easily. It affects the convergence precision of the algorithm. For the shortcoming of the algorithm, a new method, which the particles are fixed distribution to the search space, is proposed. It makes the distance among the particles, improves the searching area, increases the searching space and particles diversity, and decreases the probability of the falling into the local optimization. The experiment is shown, the comparison between the improved algorithm and the inertia weight of the particle swarm, and the improved algorithm improves the convergence precision and speed.