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  • A Study of Different Transfer Functions for Binary Version of Particle Swarm Optimization

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    Author(s)
    Mirjalili, S.
    Mohad Hashim, S.
    Taherzadeh, G.
    Mirjalili, S.
    Salehi, S.
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2011
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    Abstract
    Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost make this algorithm very popular and powerful in solving wide ranges of problems. However, PSO suffers two problems of trapping in local minima and slow convergence speed. Binary version of this algorithm has been introduced for solving binary problems. Because BPSO uses the same concepts of PSO, it also undergoes the same problems. The main part of the binary version is the transfer function. There is not enough study in the literature focusing on the transfer function. In this study, eight ...
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    Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost make this algorithm very popular and powerful in solving wide ranges of problems. However, PSO suffers two problems of trapping in local minima and slow convergence speed. Binary version of this algorithm has been introduced for solving binary problems. Because BPSO uses the same concepts of PSO, it also undergoes the same problems. The main part of the binary version is the transfer function. There is not enough study in the literature focusing on the transfer function. In this study, eight new transfer functions dividing into two families (s-shape and v-shape) for binary particle swarm optimization are introduced and evaluated. Four benchmark optimization problems are employed in order to evaluate these transfer functions in terms of avoiding local minima, convergence speed, and accuracy of results. The results prove that the new introduced v-shape family of transfer functions could improve the performance of original binary PSO based on the above-mentioned drawbacks.
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    Conference Title
    Proceedings of the 2011 International Conference on Genetic and Evolutionary Methods (GEM 11)
    Publisher URI
    http://www.world-academy-of-science.org/worldcomp11/ws/conferences/gem11
    Copyright Statement
    © 2012 CSREA. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Neural, Evolutionary and Fuzzy Computation
    Publication URI
    http://hdl.handle.net/10072/48831
    Collection
    • Conference outputs

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