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An Evolutionary Simulating Annealing Algorithm for Google Machine Reassignment Problem

Part of the Proceedings in Adaptation, Learning and Optimization book series (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.

Keywords

  • Machine Reassignment Problem
  • Simulating annealing
  • Cloud computing
  • Evolutionary algorithm

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  • DOI: 10.1007/978-3-319-49049-6_31
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Correspondence to Ayad Turky .

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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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