Dynamic Clan Particle Swarm Optimization

Particle Swarm Optimization (PSO) has been widely used to solve many different real world optimization problems. Many novel PSO approaches have been proposed to improve the PSO performance. Recently, a communication topology based on clans was proposed. In this paper, we propose the dynamic clan PSO topology. In this approach, a novel ability is included in the clan topology, named migration process. The goal is to improve the PSO degree of convergence focusing on the distribution of the particles in the search space. A comparison with the original clan topology and other well known topologies was performed and our results in five benchmark functions have shown that the changes can provide better results, except for the Rastrigin function.