A Self-adaptive Heterogeneous PSO Inspired by Ants

Heterogeneous particle swarm optimizers have been proposed where particles are allowed to implement different behaviors. A selected behavior may not be optimal for the duration of the search process. Since the optimality of a behavior depends on the fitness landscape it is necessary that particles be able to dynamically adapt their behaviors. This paper introduces two new self-adaptive heterogeneous particle swarm optimizers which are influenced by the ant colony optimization meta-heuristic. These self-adaptive strategies are compared with three other heterogeneous particle swarm optimizers. The results show that the proposed models outrank the existing models overall.

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