Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Heterogeneous particle swarm optimizers
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Heterogeneous particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
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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.