Developed and Mutated Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is an efficient tool for optimization and search problems. However, it is easy to be trapped into local optima due to its information sharing mechanism. Many research works have shown that mutation operators can also help PSO prevent premature convergence. This paper presents modifying the PSO algorithm with mutation named Developed and Mutated Particle Swarm Optimization (DMPSO). And this new algorithm has better optimization performance when solving some complex benchmark functions. The results shows that propose algorithm can achieve the goal of global minimum and maximum in limited iteration. Better positions according to the updated velocity vector [8]. A number of authors [10], [11], [12] – [14] have investigated the addition of mutation operators into PSO to improve performance, but so far, there has been no clear attempt reported in the literature to compare the effectiveness of different mutation operators. In our early work, proposed a new PSO algorithm, called DMPSO1. In this new algorithm, we got a good response with the modified PSO algorithm. First of all we multiply the inertia weight to whole of equation; and in each iteration, some of the variables are randomly mutated.

Keywords:

PSO , DMPSO , modifying , mutation , benchmark function