BSP-GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection.

The significance of Internet-of-Things to Supply Chain Management has been dramatically increasing. The performance of supply chain based on Internet-of-Things is largely dependent on its optimization. Genetic algorithms (GAs) are important intelligent methods for complex system optimization problem...

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Publicado en:Systems Research & Behavioral Science Vol. 31; no. 3; pp. 337 - 353
Autores principales: Jin, Chenxia, Li, Fachao, Wilamowska ‐ Korsak, Marzana, Li, Ling, Fu, Liuliu
Formato: Artículo
Publicado: Wiley-Blackwell May/Jun2014
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May/Jun2014
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        10.1002/sres.2280
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        atl: BSP-GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection.
      aug:
        au:
          Jin, Chenxia
          Li, Fachao
          Wilamowska ‐ Korsak, Marzana
          Li, Ling
          Fu, Liuliu
        affil:
          School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang China
          Safety Engineering Department, College of Engineering, Warmia and Mazury University at Olsztyn, Olsztyn Poland
          Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk VA, USA
          Tsinghua University, Beijing China
      su:
        Technology
        Decision making
        Genetic algorithms
        Internet of things
        Global optimization
        Supply chains
      sug:
        subj:
          Technology
          Decision making
          Genetic algorithms
          Internet of things
          Global optimization
          Supply chains
      keyword:
        excellent schema
        genetic algorithms (GA)
        global optimization
        Internet ‐ of ‐ Things (IoT)
        Internet-of-Things (IoT)
        schema theory
        excellent schema
        genetic algorithms (GA)
        global optimization
        Internet ‐ of ‐ Things (IoT)
        Internet-of-Things (IoT)
        schema theory
      ab: The significance of Internet-of-Things to Supply Chain Management has been dramatically increasing. The performance of supply chain based on Internet-of-Things is largely dependent on its optimization. Genetic algorithms (GAs) are important intelligent methods for complex system optimization problems, but they have some internal drawbacks such as premature and slow convergence to the global optimum. In this paper, we present a new schema protection based GA (BSP-GA). First, we propose three principles for selecting excellent schema based on the schema theory; second, we propose the concept of K-intensive effect synthesis operator, and we give a general five-intensive effect synthesis operator and its proof; third, we give the selection process of excellent schema through an example, and further we give the implementation steps of BSP-GA. The performance of BSP-GA has been compared with simple GA by using two carefully chosen benchmark problems. It has been observed that BSP-GA can yield the global optimum more efficiently than commonly used simple GA. Furthermore, a theorem is presented to guarantee the convergence of BSP-GA. Copyright © 2014 John Wiley & Sons, Ltd.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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