An Adaptive Tribal Topology for Particle Swarm Optimization

The success of global optimization rests on the ability for a compatible metaheuristic to approximate global search. Particle swarm optimization (PSO) is one of such heuristics, with the ideal PSO application being one that promotes swarm diversity while incorporating the global progress of the swarm in its performance. In this paper, the authors introduce an adaptive tribal topology within PSO to improve global coverage. Diversity of the swarm population was dynamically managed through an evaluation of a swarm fitness parameter, which takes into account the relative performance a swarm member and its assigned tribe has on finding better objective evaluations. The fitness function simultaneously promotes the breeding of exemplars, and the elimination of swarm members stucks in local minima. The model was evaluated on a series of benchmark problems with unique and diverse search spaces, and the results demonstrate that performance relied on the distribution and scale of the local minima present.