Advertisement

A Comparison Between Memetic Algorithm and Seeded Genetic Algorithm for Multi-objective Independent Task Scheduling on Heterogeneous Machines

  • Héctor Joaquín Fraire Huacuja
  • Alejandro Santiago
  • Johnatan E. Pecero
  • Bernabé Dorronsoro
  • Pascal Bouvry
  • José Carlos Soto Monterrubio
  • Juan Javier Gonzalez Barbosa
  • Claudia Gómez Santillan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 601)

Abstract

This chapter is focused on the problem of scheduling independent tasks on heterogeneous machines. The main contributions of our work are the following: a linear programming model to compute energy consumption for the execution of independent tasks on heterogeneous clusters, a constructive heuristic based on local search, and a new benchmark set. To assess our approach we compare the performance of two solution methods: a memetic algorithm, based on population search and local search, and a seeded genetic algorithm, based on NSGA-II. A Wilcoxon rank-sum test shows significant differences in the diversity of solutions found but not in hypervolume. The memetic algorithm gets the best diversity for a bigger instance set from the state of the art.

Keywords

Pareto Front Precedence Constraint Memetic Algorithm Linear Programming Model Optimal Makespan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

A. Santiago would like to thank CONACyT Mexico, for the support no. 360199.

References

  1. 1.
    Feng, W.-C.: The importance of being low power in high performance computing. CTWatch Q. 1(3), 11–20 (2005)Google Scholar
  2. 2.
    Center, J.W.I.R., Feitelson, D.: A survey of scheduling in multiprogrammed parallel systems. Research report, IBM T.J. Watson Research Center (1994)Google Scholar
  3. 3.
    TOP500.org.: The 43rd top 500 list published during isc14 in Leipzig, germany (2014)Google Scholar
  4. 4.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non- dominated sorting genetic algorithm for multi-objective optimization: Nsga-II. In: Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, vol. 1917 (2000)Google Scholar
  5. 5.
    Moscato, P., Cotta, C.: A modern introduction to memetic algorithms. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 141–183. Springer, US (2010)Google Scholar
  6. 6.
    Meadows, B., Riddle, P., Skinner, C., Barley, M.: Evaluating the seeding genetic algorithm. In: Cranefield, S., Nayak, A. (eds.) AI 2013: Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol. 8272, pp. 221–227. Springer International Publishing, Berlin 2013Google Scholar
  7. 7.
    Julstrom, B.A.: Seeding the population: improved performance in a genetic algorithm for the rectilinear steiner problem. In: Proceedings of the 1994 ACM Symposium on Applied Computing, SAC ’94. New York, NY, USA, pp. 222–226. ACM, New York (1994)Google Scholar
  8. 8.
    Ponterosso, P., Fox, D.S.J.: Heuristically seeded genetic algorithms applied to truss optimisation. Eng. Comput. 15(4), 345–355 (1999)CrossRefGoogle Scholar
  9. 9.
    Oman, S., Cunningham, P.: Using case retrieval to seed genetic algorithms. Int. J. Comput. Intell. Appl. 1, 71–82 (1997)CrossRefGoogle Scholar
  10. 10.
    Pineda, A.A.S., Pecero, J.E., Huacuja, H.J.F., Barbosa, J.J.G., Bouvry, P.: An iterative local search algorithm for scheduling precedence-constrained applications on heterogeneous machines. In: 6th Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2013), pp. 472–485 (2013)Google Scholar
  11. 11.
    Mokotoff, E., Jimeno, J.L.: Heuristics based on partial enumeration for the unrelated parallel processor scheduling problem. In: Annals of Operations Research, pp. 133–150. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  12. 12.
    Pecero, J.E., Bouvry, P., Barrios, C.J.: Low energy and high performance scheduling on scalable computing systems. In: Latin-American Conference on High Performance Computing, pp. 1–8 (2010)Google Scholar
  13. 13.
    Braunt, T.D., Siegel, H.J., Beck, N., Boloni, L.L., Maheswarans, M.: A comparison study of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 22, 810–837 (2001)CrossRefGoogle Scholar
  14. 14.
    Schiavinotto, T., Sttzle, T.: The linear ordering problem: instances, search space analysis and algorithms. J. Math. Model. Algorithms 3(4), 367–402 (2005)CrossRefGoogle Scholar
  15. 15.
    Villanueva, D., H. Fraire Huacuja, A. Duarte, R. Pazos R., J. Carpio Valadez, and H. Puga Soberanes, “Improving iterated local search solution for the linear ordering problem with cumulative costs (lopcc),” in Knowledge-Based and Intelligent Information and Engineering Sys- tems(R. Setchi, I. Jordanov, R. Howlett, and L. Jain, eds.), vol. 6277 of Lecture Notes in Computer Science, pp. 183–192, Springer Berlin Heidelberg, 2010Google Scholar
  16. 16.
    Topcuouglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13, 260–274 (2002)CrossRefGoogle Scholar
  17. 17.
    Lawler, E.L., Wood, D.E.: Branch-and-bound methods: a survey. Oper. Res. 14(4), 699–719 (1966)MATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Villanueva, J., Huacuja, H., Rangel, R., Valadez, J., Soberanes, H., Barbosa, J.: Iterated local search algorithm for the linear ordering problem with cumulative costs (lopcc). In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds.) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol. 318, pp. 395–404, Springer, Berlin (2011)Google Scholar
  19. 19.
    Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point. In: Foundations of Genetic Algorithms (FOGA 2009). ACM, New York (2009)Google Scholar
  20. 20.
    Veldhuizen D.A.V., Lamont, G.B.: Evolutionary computation and convergence to a Pareto front. Stanford University, California, pp. 221–228, Morgan Kaufmann (1998)Google Scholar
  21. 21.
    Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. In: Proceedings of the Congress on Evolutionary Computation. CEC, pp. 3234–3241. IEEE Press (2006)Google Scholar
  22. 22.
    Oja, H.: Nonparametric statistics with applications to science and engineering by Paul H. Kvam, Brani Vidakovic. Int. Stat. Rev. 76(1), 150–151 (2008)CrossRefGoogle Scholar
  23. 23.
    Feo, T., Resende, M.: Greedy randomized adaptive search procedures. J. Global Optim. 6(2), 109–133 (1995)MATHMathSciNetCrossRefGoogle Scholar
  24. 24.
    Resende, M.: Greedy randomized adaptive search procedures greedy randomized adaptive search procedures. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization, pp. 1460–1469, Springer, US (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Héctor Joaquín Fraire Huacuja
    • 1
  • Alejandro Santiago
    • 1
  • Johnatan E. Pecero
    • 2
  • Bernabé Dorronsoro
    • 2
  • Pascal Bouvry
    • 2
  • José Carlos Soto Monterrubio
    • 1
  • Juan Javier Gonzalez Barbosa
    • 1
  • Claudia Gómez Santillan
    • 1
  1. 1.Tecnológico Nacional de MexicoInstituto Tecnológico de Ciudad MaderoCiudad MaderoMexico
  2. 2.University of LuxembourgLuxembourgLuxembourg

Personalised recommendations