Gaussian versus Cauchy Membership Functions in Fuzzy PSO
Abstract
In standard particle swarm optimization (PSO), the best particle in each neighborhood exerts its influence over other particles in the neighborhood. Fuzzy PSO is a generalization which differs from standard PSO in the following respect: charisma (influence over others) is defined to be a fuzzy variable, and more than one particle in each neighborhood can have a non-zero degree of charisma, and, consequently, is allowed to influence others to a degree that depends on its charisma. In this paper, we compare between the use of the Gaussian and Cauchy membership functions (MF) as the MF of the charisma fuzzy variable. We evaluate the performance of the two MFs using the weighted max-sat problem.
Recommended Citation
A. M. Abdelbar et al., "Gaussian versus Cauchy Membership Functions in Fuzzy PSO," Proceedings of the IEEE International Conference on Neural Networks (2007, Orlando, FL), Institute of Electrical and Electronics Engineers (IEEE), Aug 2007.
The definitive version is available at https://doi.org/10.1109/IJCNN.2007.4371421
Meeting Name
2007 International Joint Conference on Neural Networks, IJCNN '07 (2007: Aug. 12-17, Orlando, FL)
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-1-4244-1379-9
International Standard Serial Number (ISSN)
2161-4393; 2161-4407
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Aug 2007
