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 |
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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). |
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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 |
Author_xml | – sequence: 1 givenname: Amir surname: Robati fullname: Robati, Amir email: ammirrr@yahoo.com organization: Shahid Bahonar University, Civil Engineering Department, Kerman, Iran – sequence: 2 givenname: Gholam Abbas surname: Barani fullname: Barani, Gholam Abbas organization: Civil Engineering Department, College of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran – sequence: 3 givenname: Hossein surname: Nezam Abadi Pour fullname: Nezam Abadi Pour, Hossein organization: Electrical Engineering Department, College of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran – sequence: 4 givenname: Mohammad Javad surname: Fadaee fullname: Fadaee, Mohammad Javad organization: Civil Engineering Department, College of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran – sequence: 5 givenname: Javad surname: Rahimi Pour Anaraki fullname: Rahimi Pour Anaraki, Javad organization: Shahid Bahonar University, Computer Engineering Department, Kerman, Iran |
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Keywords | Discrete optimization Membership function Balanced fuzzy Particle swarm optimization |
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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 |
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