Diversity Guided Particle Swarm Optimization algorithm based on Search Space Awareness Particle Dispersion (DGPSO)

Diversity control in the particle swarm optimization (PSO) algorithm is one of the important issues that influence the process of finding global optimal solution. In this study we create a historical process to find best area of the search space for population dispersion guide on PSO algorithm, and name Diversity Guided Particle Swarm Optimization algorithm (DGPSO) algorithm. Hence we propose a mechanism to guide the swarm based on diversity by using a diversifying process in order to detect suitable positions of the search space (points with fairly good fitness, and good distance from current distribution of the swarm particles) to disperse or relocating some of existing particles, hoped to increase diversity level of the swarm and escape from local optimal by detecting better area of the search space. This model uses a diversity measuring, and swarm dispersion mechanism to control the evolutionary process alternating between exploring and exploiting behavior. The numerical results show that the proposed algorithm outperforms other algorithms in most of the test cases taken in this study.