NDLI: Improving fuzzy cognitive maps learning through memetic particle swarm optimization

Content Provider	Springer Nature Link
Author	Petalas, Y. G. Parsopoulos, K. E. Vrahatis, M. N.
Copyright Year	2008
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.
Starting Page	77
Ending Page	94
Page Count	18
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	13
Issue Number	1
e-ISSN	14337479
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-05-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy cognitive maps Memetic algorithms Particle swarm optimization Local search Machine learning Control , Robotics, Mechatronics Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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