Abstract
In this paper, the analytical parameter tuning for the Archive Multi-objective Simulated Annealing (AMOSA) is described. The analytical tuning method yields the initial and final temperature, and the maximum metropolis length. The analytically tuned AMOSA is used to solve the Heterogeneous Computing Scheduling Problem with independent tasks and it is compared versus the AMOSA without parameter tuning. We approach this problem as multi-objective, considering the makespan and the energy consumption. Also, in the last years this problem has gained importance due to the energy awareness in high performance computing centers (HPCC). The hypervolume, generational distance, and spread metrics were used in order to measure the performance of the implemented algorithms.
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References
W. C. Feng, “The importance of being low power in high performance computing,” CT Watch Quarterly, vol. 1, no. 3, pp. 11-20, 2005.
Y. Liu and H. Zhu, “A survey of the research on power management techniques for high-performance systems,” Software: Practice and Experience, p. 943–964, 2010.
G. Magklis, G. Semeraro, D. H. Albonesi, S. G. Dropsho, S. Dwarkadas and M. L. Scott, “Dynamic frequency and voltage scaling for a multiple-clock-domain microprocessor,” Micro, IEEE, vol. 23, no. 6, pp. 62-68, 2003.
S. Bandyopadhyay, S. Saha, U. Maulik and K. Deb, “A simulated annealing-based multiobjective optimization algorithm: AMOSA},,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 3, pp. 269-283, 2008.
J. Frausto-Solís, H. Sanvicente-Sánchez and F. Imperial-Valenzuela, “ANDYMARK: an analytical method to establish dynamically the length of the markov chain in simulated annealing for the satisfiability problem,” in Simulated Evolution and Learning, Springer, 2006, pp. 269-276.
J. Frausto-Solis, J. P. Sánchez-Hernández, M. Sánchez-Pérez and E. L. García, “Golden Ratio Simulated Annealing for Protein Folding Problem,” International Journal of Computational Methods, 2015.
J. Frausto-Solis, E. Liñan-García, M. Sánchez-Pérez and J. P. Sánchez-Hernández, “Chaotic Multiquenching Annealing Applied to the Protein Folding Problem,” The Scientific World Journal, vol. 2014, pp. 1-12, 2014.
S. Kirkpatrick, “Optimization by simulated annealing: Quantitative studies,” Journal of statistical physics, vol. 34, no. 5-6, pp. 975–986, 1984.
P. Serafini, “Simulated annealing for multi objective optimization problems,” in Multiple criteria decision making, Springer, 1993, pp. 283-292.
D. Nam and C. H. Park, “Multiobjective simulated annealing: A comparative study to evolutionary algorithms,” International Journal of Fuzzy Systems, vol. 2, no. 2, pp. 87-97, 2000.
M. Alrefaei, A. Diabat, A. Alawneh, R. Al-Aomar and M. N. Faisal, “Simulated annealing for multi objective stochastic optimization,” International Journal of Science and Applied Information Technology, vol. 2, pp. 18-21, 2013.
Y. Xu, R. Qu and R. Li, “A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems,” Annals of Operations Research, vol. 206, no. 1, pp. 527-555, 2013.
A. Zaretalab, V. Hajipour, M. Sharifi and M. R. Shahriari, “A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies,” Computers & Industrial Engineering, vol. 80, pp. 33-44, 2015.
S.-W. Lin and K.-C. Ying, “A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems,” International Journal of Production Research, vol. 53, no. 4, pp. 1065-1076, 2015.
A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1988.
K. Deb and M. Goyal, “A combined genetic adaptive search (GeneAS) for engineering design,” Computer Science and Informatics, pp. 30-45, 1996.
J. Wu and S. Azarm, “Metrics for quality assessment of a multiobjective design optimization solution set,” Journal of Mechanical Design, vol. 123, no. 1, pp. 18-25, 2001.
Acknowledgments
The authors would like to acknowledge with appreciation and gratitude to CONACYT. This work has been partial supported by CONACYT Project.254498 and PRODEP.
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Fraire Huacuja, H.J., Frausto-Solís, J., Terán-Villanueva, J.D., Soto-Monterrubio, J.C., González Barbosa, J.J., Castilla-Valdez, G. (2017). AMOSA with Analytical Tuning Parameters for Heterogeneous Computing Scheduling Problem. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_46
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DOI: https://doi.org/10.1007/978-3-319-47054-2_46
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