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Improving fuzzy cognitive maps learning through memetic particle swarm optimization

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Abstract

Fuzzy cognitive maps constitute a neuro-fuzzy modeling methodology that can simulate complex systems accurately. Although their configuration is defined by experts, learning schemes based on evolutionary and swarm intelligence algorithms have been employed for improving their efficiency and effectiveness. This paper comprises an extensive study of the recently proposed swarm intelligence memetic algorithm that combines particle swarm optimization with both deterministic and stochastic local search schemes, for fuzzy cognitive maps learning tasks. Also, a new technique for the adaptation of the memetic schemes, with respect to the available number of function evaluations per application of the local search, is proposed. The memetic learning schemes are applied on four real-life problems and compared with established learning methods based on the standard particle swarm optimization, differential evolution, and genetic algorithms, justifying their superiority.

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Authors and Affiliations

  1. Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, 26110, Patras, Greece

    Y. G. Petalas, K. E. Parsopoulos & M. N. Vrahatis

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  1. Y. G. Petalas
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  2. K. E. Parsopoulos
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  3. M. N. Vrahatis
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Correspondence to M. N. Vrahatis.

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Petalas, Y.G., Parsopoulos, K.E. & Vrahatis, M.N. Improving fuzzy cognitive maps learning through memetic particle swarm optimization. Soft Comput 13, 77–94 (2009). https://doi.org/10.1007/s00500-008-0311-2

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  • DOI: https://doi.org/10.1007/s00500-008-0311-2

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