Abstract
In standard particle swarm optimization (PSO), the best particle in each neighborhood exerts its influence over other particles in the neighborhood. In this paper, we propose fuzzy PSO, a generalization which differs from standard PSO in the following respect: charisma 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. We evaluate our model on the weighted maximum satisfiability (maxsat) problem, comparing performance to standard PSO and to Walk-Sat.
Recommended Citation
S. Abdelshahid et al., "Fuzzy PSO: A Generalization of Particle Swarm Optimization," Proceedings of the IEEE International Joint Conference on Neural Networks, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/IJCNN.2005.1556004
Meeting Name
IEEE International Joint Conference on Neural Networks, 2005
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Charisma; Computability; Fuzzy Set Theory; Fuzzy Variable; Particle Swarm Optimisation; Particle Swarm Optimization; Weighted Maximum Satisfiability Problem
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2005
