Including Dynamic Adaptative Topology to Particle Swarm Optimization Algorithms

Resumen

Particle Swarm Optimization algorithms (or PSO) have been widely studied in the Literature. It is known that they provide highly competitive results. However, they suffer from fast convergence to local optima. There exist works proposing the swarm decentralization by including some specific topologies in order to deal with this problem. These approaches highly improve the results. In this work, we propose PSO-CO, a PSO algorithm able to reduce the exploitation of the algorithm by introducing the concept of coalitions in the swarm. There is one leader in each of these coalitions, so that the particles belonging to a coalition are only influenced by their local leader, and not the global one. This mechanism allows different coalitions to explore different parts of the search space, reducing thus the convergence speed and enhancing the exploration capabilities of the algorithm. Moreover, the particles can leave a coalition and join another, facilitating the exchange of information between coalitions. For testing the efficiency of the proposed PSO-CO, we have chosen a relevant benchmark in the literature, specially designed for continuous optimization. Results show that PSO-CO highly improves the results obtained compared to classical PSO.