A Particle Swarm Optimization Algorithm with Crossover Operator

Particle swarm optimization (PSO) is a method for tackling optimization functions. However, it is easily trapped into the local optimization when solving high-dimension functions. To overcome this shortcoming, a modified particle swarm optimization is proposed in this paper. In the proposed method, a crossover step is added to the standard PSO. The crossover is taken between each particle´s individual best position. After the crossover, the fitness of the individual best position is compared with that of the two offspring, and the best one is taken as the new individual best position. The crossover can help the particles jump out of the local optimization by sharing the others´ information. The experiment on five benchmark functions shows that the modified PSO is more effective to find the global optimal solution than other methods.