# Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem

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## 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.

## Keywords

Machine reassignment problem Simulated annealing Cloud computing Cooperative search## References

- 1.Roadef/euro Challenge 2012: Machine Reassignment. http://challenge.roadef.org/2012/en/
- 2.H.M. Afsar, C. Artigues, E. Bourreau, S. Kedad-Sidhoum, Machine reassignment problem: the ROADEF/EURO challenge 2012. Ann. Oper. Res.
**242**, 1–17 (2016)MathSciNetCrossRefzbMATHGoogle Scholar - 3.E. Alba,
*Parallel Metaheuristics: A New Class of Algorithms*, vol. 47 (Wiley, New York, 2005)CrossRefzbMATHGoogle Scholar - 4.E. Alba, G. Luque, S. Nesmachnow, Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res.
**20**(1), 1–48 (2013)CrossRefzbMATHGoogle Scholar - 5.M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica et al., A view of cloud computing. Commun. ACM
**53**(4), 50–58 (2010)CrossRefGoogle Scholar - 6.P. Badeau, F. Guertin, M. Gendreau, J.-Y. Potvin, E. Taillard, A parallel tabu search heuristic for the vehicle routing problem with time windows. Transp. Res. Part C Emerg. Technol.
**5**(2), 109–122 (1997)CrossRefzbMATHGoogle Scholar - 7.R. Bellio, S. Ceschia, L. Di Gaspero, A. Schaerf, T. Urli, Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem. Comput. Oper. Res.
**65**, 83–92 (2016)MathSciNetCrossRefzbMATHGoogle Scholar - 8.F. Brandt, J. Speck, M. Völker, Constraint-based large neighborhood search for machine reassignment. Ann. Oper. Res.
**242**(1), 63–91 (2012)CrossRefzbMATHGoogle Scholar - 9.R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A. De Rose, R. Buyya, Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp.
**41**(1), 23–50 (2011)CrossRefGoogle Scholar - 10.T.G. Crainic, M. Toulouse, Parallel meta-heuristics. in
*Handbook of Metaheuristics*, 2nd edn, ed. by M. Gendreau, J.Y. Potvin (Springer, 2010), pp. 497–541Google Scholar - 11.M. El-Abd, M. Kamel, A taxonomy of cooperative search algorithms. in
*International Workshop on Hybrid Metaheuristics*(Springer, 2005), pp. 32–41Google Scholar - 12.S. García, A. Fernández, J. Luengo, F. Herrera, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci.
**180**(10), 2044–2064 (2010)CrossRefGoogle Scholar - 13.H. Gavranović, M. Buljubašić, E. Demirović, Variable neighborhood search for google machine reassignment problem. Electron. Notes Discret. Math.
**39**, 209–216 (2012)CrossRefzbMATHGoogle Scholar - 14.S. Huda, J. Yearwood, R. Togneri, Hybrid metaheuristic approaches to the expectation maximization for estimation of the hidden markov model for signal modeling. Cybern. IEEE Trans.
**44**(10), 1962–1977 (2014)CrossRefGoogle Scholar - 15.G. Kendall, R. Bai, J. Błazewicz, P. De Causmaecker, M. Gendreau, R. John, J. Li, B. McCollum, E. Pesch, R. Qu et al., Good laboratory practice for optimization research. J. Oper. Res. Soc.
**67**(4), 676–689 (2016)CrossRefGoogle Scholar - 16.S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi et al., Optimization by simulated annealing. Science
**220**(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar - 17.D. Landa-Silva, E.K. Burke, Asynchronous cooperative local search for the office-space-allocation problem. INFORMS J. Comput.
**19**(4), 575–587 (2007)MathSciNetCrossRefzbMATHGoogle Scholar - 18.S.-W. Lin, F.Y. Vincent, A simulated annealing heuristic for the multiconstraint team orienteering problem with multiple time windows. Appl. Soft Comput.
**37**, 632–642 (2015)CrossRefGoogle Scholar - 19.R. Lopes, V.W. Morais, T.F. Noronha, V.A. Souza, Heuristics and matheuristics for a real-life machine reassignment problem. Int. Trans. Oper. Res.
**22**(1), 77–95 (2015)MathSciNetCrossRefzbMATHGoogle Scholar - 20.Z. Lou, J. Reinitz, Parallel simulated annealing using an adaptive resampling interval. Parallel Comput.
**53**, 23–31 (2016)MathSciNetCrossRefGoogle Scholar - 21.Y. Malitsky, D. Mehta, B. O’Sullivan, H. Simonis, Tuning parameters of large neighborhood search for the machine reassignment problem. in
*Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems*(Springer, 2013), pp. 176–192Google Scholar - 22.R. Martí, M.G. Resende, C.C. Ribeiro, Multi-start methods for combinatorial optimization. Eur. J. Oper. Res.
**226**(1), 1–8 (2013)MathSciNetCrossRefzbMATHGoogle Scholar - 23.R. Masson, T. Vidal, J. Michallet, P.H.V. Penna, V. Petrucci, A. Subramanian, H. Dubedout, An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems. Expert Syst. Appl.
**40**(13), 5266–5275 (2013)CrossRefGoogle Scholar - 24.D. Mehta, B. O’Sullivan, H. Simonis, Comparing solution methods for the machine reassignment problem. in
*Principles and Practice of Constraint Programming*. Lecture Notes in Computer Science, ed. by M. Milano (Springer, Berlin, Heidelberg, 2012), pp. 782–797Google Scholar - 25.Y. Mei, K. Tang, X. Yao, Decomposition-based memetic algorithm for multiobjective capacitated arc routing problem. IEEE Trans. Evol. Comput.
**15**(2), 151–165 (2011)CrossRefGoogle Scholar - 26.Y. Mei, K. Tang, X. Yao, A memetic algorithm for periodic capacitated arc routing problem. IEEE Trans. Syst. Man Cybern. Part B (Cybern.)
**41**(6), 1654–1667 (2011)CrossRefGoogle Scholar - 27.M. Mrad, A. Gharbi, M. Haouari, M. Kharbeche, An optimization-based heuristic for the machine reassignment problem. Ann. Oper. Res.
**242**(1), 115–132 (2015)MathSciNetCrossRefzbMATHGoogle Scholar - 28.D. Mu, C. Wang, F. Zhao, J.W. Sutherland, Solving vehicle routing problem with simultaneous pickup and delivery using parallel simulated annealing algorithm. Int. J. Ship. Transp. Logist.
**8**(1), 81–106 (2016)CrossRefGoogle Scholar - 29.F. Neri, C. Cotta, Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol. Comput.
**2**, 1–14 (2012)CrossRefGoogle Scholar - 30.S. Pace, A. Turky, I. Moser, A. Aleti, Distributing fibre boards: a practical application of the heterogeneous fleet vehicle routing problem with time windows and three-dimensional loading constraints. Proced. Comput. Sci.
**51**, 2257–2266 (2015)CrossRefGoogle Scholar - 31.K. Peng, Y. Shen, J. Li, A multi-objective simulated annealing for bus driver rostering. in
*Bio-Inspired Computing—Theories and Applications*. Communications in Computer and Information Science, vol. 562, ed. by M. Gong, L. Pan, T. Song, K. Tang, X. Zhang (Springer, Berlin, Heidelberg, 2015), pp. 315–330Google Scholar - 32.G.M. Portal, M. Ritt, L. Borba, L.S. Buriol, Simulated annealing for the machine reassignment problem. Ann. Oper. Res.
**242**(1), 93–114 (2016)MathSciNetCrossRefzbMATHGoogle Scholar - 33.M.R.P. Ritt,
*An Algorithmic Study of the Machine Reassignment Problem*. PhD Thesis, Universidade Federal do Rio Grande do Sul (2012)Google Scholar - 34.N. Sabar, J. Abawajy, J. Yearwood, Heterogeneous cooperative co-evolution memetic differential evolution algorithms for big data optimisation problems. IEEE Trans. Evol. Comput.
**21**(2), 315–327 (2017)CrossRefGoogle Scholar - 35.N.R. Sabar, A. Aleti, An adaptive memetic algorithm for the architecture optimisation problem. in
*Australasian Conference on Artificial Life and Computational Intelligence*(Springer, 2017), pp. 254–265Google Scholar - 36.N.R. Sabar, M. Ayob, G. Kendall, R. Qu, Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Trans. Evol. Comput.
**17**(6), 840–861 (2013)CrossRefGoogle Scholar - 37.N.R. Sabar, M. Ayob, G. Kendall, R. Qu, Automatic design of a hyper-heuristic framework with gene expression programming for combinatorial optimization problems. IEEE Trans. Evol. Comput.
**19**(3), 309–325 (2015)CrossRefGoogle Scholar - 38.N.R. Sabar, M. Ayob, G. Kendall, R. Qu, A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems. IEEE Trans. Cybern.
**45**(2), 217–228 (2015)CrossRefGoogle Scholar - 39.N.R. Sabar, G. Kendall, An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem. Omega
**56**, 88–98 (2015)CrossRefGoogle Scholar - 40.N.R. Sabar, A. Song, Grammatical evolution enhancing simulated annealing for the load balancing problem in cloud computing. in
*Proceedings of the 2016 on Genetic and Evolutionary Computation Conference ACM*(2016), pp. 997–1003Google Scholar - 41.N.R. Sabar, A. Song, Z. Tari, X. Yi, A. Zomaya, A memetic algorithm for dynamic shortest path routing on mobile ad-hoc networks. in
*Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on IEEE*(2015), pp. 60–67Google Scholar - 42.N.R. Sabar, A. Song, M. Zhang, A variable local search based memetic algorithm for the load balancing problem in cloud computing. in
*European Conference on the Applications of Evolutionary Computation*(Springer, 2016), pp. 267–282Google Scholar - 43.N.R Sabar, A. Turky, A. Song, A multi-memory multi-population memetic algorithm for dynamic shortest path routing in mobile ad-hoc networks. in
*Pacific Rim International Conference on Artificial Intelligence*(Springer, 2016), pp. 406–418Google Scholar - 44.J. Sun, Q. Zhang, X. Yao, Meta-heuristic combining prior online and offline information for the quadratic assignment problem. Cybern. IEEE Trans.
**44**(3), 429–444 (2014)CrossRefGoogle Scholar - 45.A. Turky, N.R. Sabar, A. Sattar, A. Song, Parallel late acceptance hill-climbing algorithm for the google machine reassignment problem. in
*Australasian Joint Conference on Artificial Intelligence*(Springer, 2016), pp. 163–174Google Scholar - 46.A. Turky, N.R. Sabar, A. Song, A multi-population memetic algorithm for dynamic shortest path routing in mobile ad-hoc networks. in
*Evolutionary Computation (CEC), 2016 IEEE Congress on IEEE*(2016), pp. 4119–4126Google Scholar - 47.A. Turky, N.R. Sabar, A. Song, An evolutionary simulated annealing algorithm for google machine reassignment problem. in
*Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016*, Canberra, Australia, November 2016, Proceedings (Springer, 2017), pp. 431–442Google Scholar - 48.A. Turky, N.R. Sabar, A. Song, Neighbourhood analysis: a case study on google machine reassignment problem. in
*Australasian Conference on Artificial Life and Computational Intelligence*(Springer, 2017), pp. 228–237Google Scholar - 49.F.Y. Vincent, S.-Y. Lin, A simulated annealing heuristic for the open location-routing problem. Comput. Oper. Res.
**62**, 184–196 (2015)MathSciNetCrossRefzbMATHGoogle Scholar - 50.J. Wang, W. Zhang, J. Zhang, Cooperative differential evolution with multiple populations for multiobjective optimization. IEEE Trans. Cybern.
**46**(12), 2848–2861 (2016)CrossRefGoogle Scholar - 51.Z. Wang, Z. Lü, T. Ye, Multi-neighborhood local search optimization for machine reassignment problem. Comput. Oper. Res.
**68**, 16–29 (2016)MathSciNetCrossRefzbMATHGoogle Scholar - 52.S. Xavier-de Souza, J.A. Suykens, J. Vandewalle, D. Bollé, D. Bollé, Coupled simulated annealing. Syst. Man Cybern. Part B Cybern. IEEE Trans.
**40**(2), 320–335 (2010)CrossRefGoogle Scholar