Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem

This paper investigates the Google machine reassignment problem (GMRP). GMRP is a real world optimisation problem which is to maximise the usage of cloud machines. Since GMRP is computationally challenging problem and exact methods are only advisable for small instances, meta-heuristic algorithms ha...

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Bibliographic Details
Published in:Genetic programming and evolvable machines 2018-06, Vol.19 (1-2), p.183-210
Main Authors: Turky, Ayad, Sabar, Nasser R., Song, Andy
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Computer simulation
Electrical Engineering
Evolutionary algorithms
Heuristic methods
Interpreters
Programming Languages
Programming Techniques
Simulated annealing
Software Engineering/Programming and Operating Systems
Solution space
Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation
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Summary:This paper investigates the Google machine reassignment problem (GMRP). GMRP is a real world optimisation problem which is to maximise the usage of cloud machines. Since GMRP is computationally challenging problem and exact methods are only advisable for small instances, meta-heuristic algorithms have been used to address medium and large instances. This paper proposes a cooperative evolutionary heterogeneous simulated annealing (CHSA) algorithm for GMRP. The proposed algorithm consists of several components devised to generate high quality solutions. Firstly, a population of solutions is used to effectively explore the solution space. Secondly, CHSA uses a pool of heterogeneous simulated annealing algorithms in which each one starts from a different initial solution and has its own configuration. Thirdly, a cooperative mechanism is designed to allow parallel searches to share their best solutions. Finally, a restart strategy based on mutation operators is proposed to improve the search performance and diversification. The evaluation on 30 diverse real-world instances shows that the proposed CHSA performs better compared to cooperative homogeneous SA and heterogeneous SA with no cooperation. In addition, CHSA outperformed the current state-of-the-art algorithms, providing new best solutions for eleven instances. The analysis on algorithm behaviour clearly shows the benefits of the cooperative heterogeneous approach on search performance.
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-017-9305-0