Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function

    Access Status
    Fulltext not available
    Authors
    Lu, H.
    Sriyanyong, P.
    Song, Y.
    Dillon, Tharam S.
    Date
    2010
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Lu, Haiyan and Sriyanyong, Pichet and Song, Yong Hua and Dillon, Tharam S. 2010. Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function. Electrical Power and Energy Systems. 32 (9): pp. 921-935.
    Source Title
    Electrical Power and Energy Systems
    DOI
    10.1016/j.ijepes.2010.03.001
    ISSN
    01420615
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/11699
    Collection
    • Curtin Research Publications
    Abstract

    Particle swarm optimization (PSO) is a population-based evolutionary technique. Advancements in the PSO development over the last decade have made it one of the most promising optimization algorithms for a wide range of complex engineering optimization problems which traditional derivative-based optimization techniques cannot handle. The most attractive features of PSO are its algorithmic simplicity and fast convergence. However, PSO tends to suffer from premature convergence when applied to strongly multi-modal optimization problems. This paper proposes a method of incorporating a real-valued mutation (RVM) operator into the PSO algorithms, aimed at enhancing global search capability. Three variants of PSO algorithms are considered. The resultant hybrid PSO-RVM algorithms are experimentally investigated along with the PSO variants and an existing PSO with Gaussian mutation using six typical benchmark functions.It is interesting to see that the effectiveness of RVM varies for different PSO variants as well as different kinds of functions. It has been found that one of the hybrid algorithms, CBPSO-RVM, which is an integration of the PSO with the constriction factor and inertia weight (CBPSO) and the RVM operator, exhibits significantly better performance in most of the test cases compared to the other algorithms under consideration. Furthermore, this algorithm is superior to most of the existing algorithms used in this study when applied to two practical ED problems with non-smooth cost function considering the multiple fuel type and/or valve-point loading effects.

    Related items

    Showing items related by title, author, creator and subject.

    • Heuristic algorithms for routing problems.
      Chong, Yen N. (2001)
      General routing problems deal with transporting some commodities and/or travelling along the axes of a given network in some optimal manner. In the modern world such problems arise in several contexts such as distribution ...
    • Global algorithms for nonlinear discrete optimization and discrete-valued optimal control problems
      Woon, Siew Fang (2009)
      Optimal control problems arise in many applications, such as in economics, finance, process engineering, and robotics. Some optimal control problems involve a control which takes values from a discrete set. These problems ...
    • Permutation flow shop scheduling: fuzzy particle swarm optimization approach
      Ling, S.; Jiang, F.; Chan, Kit Yan; Nguyen, H. (2011)
      A fuzzy particle swarm optimization (PSO) for the minimization of makespan in permutation flow shop scheduling problem is presented in this paper. In the proposed fuzzy PSO, the inertia weight of PSO and the control ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.