Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem

Turky, A, Sabar, N and Song, A 2018, 'Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem', Genetic Programming and Evolvable Machines, vol. 19, no. 12, pp. 183-210.


Document type: Journal Article
Collection: Journal Articles

Title Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem
Author(s) Turky, A
Sabar, N
Song, A
Year 2018
Journal name Genetic Programming and Evolvable Machines
Volume number 19
Issue number 12
Start page 183
End page 210
Total pages 28
Publisher Springer
Abstract 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.
Subject Neural, Evolutionary and Fuzzy Computation
Keyword(s) Cloud computing
Cooperative search
Machine reassignment problem
Simulated annealing
DOI - identifier 10.1007/s10710-017-9305-0
Copyright notice © Springer Science+Business Media New York 2017
ISSN 1389-2576
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