Abstract
This paper uses the emergent properties of a Self-Organized Criticality (SOC) system for controlling the inertia weight of the Particle Swarm Optimization (PSO) algorithm. The strategy is based on the SOC Bak-Sneppen model of co-evolution. In this model, an ecosystem is simulated by a population of species with random fitness connected in a ring topology. In each time-step, the worst species and its neighbors are randomly mutated. The threshold fitness of the model, which is the highest level the lowest fitness has reached, is used in this paper for controlling the inertia weight. The resulting algorithm is named Bak-Sneppen threshold PSO (BSt-PSO). An experimental setup compares the new algorithm with versions of the PSO with varying inertia weight, including a state-of-the-art algorithm with dynamic variation of the parameters. The results demonstrate that the BSt-PSO is clearly faster than the other algorithms in meeting the convergence criteria.
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Fernandes, C.M., Merelo, J.J., Rosa, A.C. (2015). A Time-Varying Inertia Weight Strategy for Particles Swarms Based on Self-Organized Criticality. In: Madani, K., Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2012. Studies in Computational Intelligence, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-11271-8_4
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DOI: https://doi.org/10.1007/978-3-319-11271-8_4
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