Improved PSO Algorithm and its Application in Function Optimization

To solve the problem in function optimization using Particle Swarm Optimization algorithm (PSO), this paper propose an improved PSO algorithm adopting gradient information. There are some merits such as simplicity, high effectiveness and so on about function optimization using PSO algorithm. But some studies shows that some shortcomings such as slow computing speed, easiness to fall in local peak in large scale problem are appeared in the optimization, as is determined by the randomness of the algorithm. The grad algorithm is a type of traditional optimization way and has the characteristic which is along the descending grad direction of optimization values. So the grad method can reduce the time for the optimization values because the direction for optimization values is determined by the grad of grad algorithm. In order to overcome the disadvantages of the standard PSO algorithm, the principle of grad method was used in PSO algorithm. Therefore, the Grads-PSO algorithm (regulated by grad method) was proposed in this paper. The Grads-PSO has been compared by the ones of the standard PSO algorithm. The simulation results show that the improved PSO algorithm increase the computing speed than the standard PSO algorithm.