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Chaotic Walk in Simulated Annealing Search Space for Task Allocation in a Multiprocessing System

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  • Ken Ferens

    (Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada)

  • Darcy Cook

    (JCA Electronics, Winnipeg, Canada)

  • Witold Kinsner

    (Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada)

Abstract

This paper proposes the application of chaos in large search space problems, and suggests that this represents the next evolutionary step in the development of adaptive and intelligent systems towards cognitive machines and systems. Three different versions of chaotic simulated annealing (XSA) were applied to combinatorial optimization problems in multiprocessor task allocation. Chaotic walks in the solution space were taken to search for the global optimum or “good enough” task-to-processor allocation solutions. Chaotic variables were generated to set the number of perturbations made in each iteration of a XSA algorithm. In addition, parameters of a chaotic variable generator were adjusted to create different chaotic distributions with which to search the solution space. The results show that the convergence rate of the XSA algorithm is faster than simulated annealing when the solutions are far apart in the solution space. In particular, the XSA algorithms found simulated annealing’s best result on average about 4 times faster than simulated annealing.

Suggested Citation

  • Ken Ferens & Darcy Cook & Witold Kinsner, 2013. "Chaotic Walk in Simulated Annealing Search Space for Task Allocation in a Multiprocessing System," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 7(3), pages 58-79, July.
    • Handle: RePEc:igg:jcini0:v:7:y:2013:i:3:p:58-79
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