Improved particle swarm optimization algorithms

In this paper, first of all, we introduce the normal particle swarm optimization algorithms (PSO), for this kind of algorithms, there are problems like it easily stuck at a local minimum point and its convergence speed is slow. To overcome this, an improved particle swarm optimization algorithms is presented for improving global and local search ability of PSO. That is, the rate of particle convergence changing was introduced in this new algorithm and the inertia weight was formulated as a function of this factor according to its impact on the search performance of the swarm to adjust its convergence speed and jump over local minimum points. To show effectiveness of this method, the simulations of four benchmark examples are carried out by the proposed method, as a result, this indicates that the proposed method is very useful.