Balanced fuzzy particle swarm optimization

In the present study an extension of particle swarm optimization (PSO) algorithm which is in conformity with actual nature is introduced for solving combinatorial optimization problems. Development of this algorithm is essentially based on balanced fuzzy sets theory. The classical fuzzy sets theory...

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Published in:Applied mathematical modelling Vol. 36; no. 5; pp. 2169 - 2177
Main Authors: Robati, Amir, Barani, Gholam Abbas, Nezam Abadi Pour, Hossein, Fadaee, Mohammad Javad, Rahimi Pour Anaraki, Javad
Format: Journal Article
Language:English
Published: Elsevier Inc 01-05-2012
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Abstract In the present study an extension of particle swarm optimization (PSO) algorithm which is in conformity with actual nature is introduced for solving combinatorial optimization problems. Development of this algorithm is essentially based on balanced fuzzy sets theory. The classical fuzzy sets theory cannot distinguish differences between positive and negative information of membership functions, while in the new method both kinds of information “positive and negative” about membership function are equally important. The balanced fuzzy particle swarm optimization algorithm is used for fundamental optimization problem entitled traveling salesman problem (TSP). For convergence inspecting of new algorithm, method was used for TSP problems. Convergence curves were represented fast convergence in restricted and low iterations for balanced fuzzy particle swarm optimization algorithm (BF-PSO) comparison with fuzzy particle swarm optimization algorithm (F-PSO).
AbstractList In the present study an extension of particle swarm optimization (PSO) algorithm which is in conformity with actual nature is introduced for solving combinatorial optimization problems. Development of this algorithm is essentially based on balanced fuzzy sets theory. The classical fuzzy sets theory cannot distinguish differences between positive and negative information of membership functions, while in the new method both kinds of information "positive and negative" about membership function are equally important. The balanced fuzzy particle swarm optimization algorithm is used for fundamental optimization problem entitled traveling salesman problem (TSP). For convergence inspecting of new algorithm, method was used for TSP problems. Convergence curves were represented fast convergence in restricted and low iterations for balanced fuzzy particle swarm optimization algorithm (BF-PSO) comparison with fuzzy particle swarm optimization algorithm (F-PSO).
Author Fadaee, Mohammad Javad
Barani, Gholam Abbas
Robati, Amir
Nezam Abadi Pour, Hossein
Rahimi Pour Anaraki, Javad
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Issue 5
Keywords Discrete optimization
Membership function
Balanced fuzzy
Particle swarm optimization
Language English
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Snippet In the present study an extension of particle swarm optimization (PSO) algorithm which is in conformity with actual nature is introduced for solving...
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SubjectTerms Algorithms
Balanced fuzzy
Balancing
Combinatorial analysis
Discrete optimization
Fuzzy
Fuzzy logic
Fuzzy set theory
Mathematical models
Membership function
Optimization
Particle swarm optimization
Title Balanced fuzzy particle swarm optimization
URI https://dx.doi.org/10.1016/j.apm.2011.08.006
https://search.proquest.com/docview/1010903561
Volume 36
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