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An Evolutionary Simulating Annealing Algorithm for Google Machine Reassignment Problem

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

Abstract

Google Machine Reassignment Problem (GMRP) is a real world problem proposed at ROADEF/EURO challenge 2012 competition which must be solved within 5 min. GMRP consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. This paper proposes an evolutionary simulating annealing (ESA) algorithm for solving this problem. Simulating annealing (SA) is a single solution based heuristic, which has been successfully used in various optimisation problems. The proposed ESA uses a population of solutions instead of a single solution. Each solution has its own SA algorithm and all SAs work in parallel manner. Each SA starts with different initial solution which can lead to a different search path with distinct local optima. In addition, mutation operators are applied once the solution cannot be improved for a certain number of iterations. This will not only help the search avoid being trapped in a local optima, but also reduce computation time. Because new solutions are not generated from scratch but based on existing ones. This study shows that the proposed ESA method can outperform state of the art algorithms on GMRP.

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Author information

Authors and Affiliations

  1. School of Computer Science and I.T., RMIT University, Melbourne, Australia

    Ayad Turky & Andy Song

  2. Queensland University of Technology, Brisbane, Australia

    Nasser R. Sabar

Authors
  1. Ayad Turky
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  2. Nasser R. Sabar
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  3. Andy Song
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Corresponding author

Correspondence to Ayad Turky .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    George Leu

  2. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Hemant Kumar Singh

  3. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Saber Elsayed

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© 2017 Springer International Publishing AG

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Turky, A., Sabar, N.R., Song, A. (2017). An Evolutionary Simulating Annealing Algorithm for Google Machine Reassignment Problem. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_31

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  • DOI: https://doi.org/10.1007/978-3-319-49049-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

  • eBook Packages: EngineeringEngineering (R0)

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