HydroCM: A Hybrid Parallel Search Model for Heterogeneous Platforms

  • Julián Domínguez
  • Enrique Alba
Part of the Studies in Computational Intelligence book series (SCI, volume 434)


Here we present HydroCM (HydroCarbon inspired Metaheuristic), a parallel metaheuristic model specifically designed for its execution on heterogeneous hardware environments. With HydroCM we actually propose a schema for describing a family of parallel hybrid metaheuristics inspired by the structure of hydrocarbons in Nature, establishing a resemblance between atoms and computers, and between chemical bonds and communication links. Our goal is to gracefully match computers of different computing power to algorithms of different behavior (GA and SA in this study), all them collaborating to solve the same problem. The analysis will show that our proposal, though simple, can solve search problems in a faster and more robust way than well-known panmictic and distributed algorithms very popular in the literature.


Genetic Algorithm Simulated Annealing Numerical Effort Single Processor Slave Node 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aarts, E.H.L., Verhoeven, M.G.A.: Genetic local search for the traveling salesman problem. In: Handbook of Evolutionary Computation, pp. G9.5:1–7. Institute of Physics Publishing and Oxford University Press (1997)Google Scholar
  2. 2.
    Alba, E.: Parallel evolutionary algorithms can achieve super-lineal performance. Information Processing Letters 82, 7–13 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Alba, E.: Metaheuristics and Parallelism. In: Parallel Metaheuristics: A new Class of Algorithms, pp. 79–103. Wiley-Interscience (2005)Google Scholar
  4. 4.
    Alba, E.: Parallel Heterogeneous Metaheuristics. In: Parallel Metaheuristics: A new Class of Algorithms, pp. 395–422. Wiley-Interscience (2005)Google Scholar
  5. 5.
    Alba, E., Dorronsoro, B.: The State of the Art in Cellular Evolutionary Algorithms. In: Cellular Genetic Algorithms, pp. 21–34. Springer, US (2008)CrossRefGoogle Scholar
  6. 6.
    Alba, E., Luna, F., Nebro, A.J., Troya, J.M.: Parallel heterogeneous genetic algorithms for continuous optimization. Parallel Computing 30(5-6), 699–719 (2004)CrossRefGoogle Scholar
  7. 7.
    Alba, E., Nebro, A.J., Troya, J.M.: Heterogeneous Computing and Parallel Genetic Algorithms. Journal of Parallel and Distributed Computing 62, 1362–1385 (2002)zbMATHCrossRefGoogle Scholar
  8. 8.
    Alba, E., Troya, J.M.: Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Generation Computer Systems 17, 451–465 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Branke, J., Kamper, A., Schmeck, H.: Distribution of Evolutionary Algorithms in Heterogeneous Networks. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 923–934. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Chen, H., Flann, N.S.: Parallel Simulated Annealing and Genetic Algorithms: A Space of Hybrid Methods. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, Springer, Heidelberg (1994)Google Scholar
  11. 11.
    Crainic, T.G., Toulouse, M.: Parallel strategies for meta-heuristics. In: Handbook of Metaheuristics, pp. 474–513. Kluwer (2003)Google Scholar
  12. 12.
    De Falco, I., Del Balio, R., Tarantino, E., Vaccaro, R.: Improving search by incorporating evolution principles in parallel tabu search. In: Int. Conf. on Machine Learning, pp. 823–828 (1994)Google Scholar
  13. 13.
    Domínguez, J., Alba, E.: Ethane: A Heterogeneous Parallel Search Algorithm for Heterogeneous Platforms. In: DECIE (2011), doi:arXiv:1105.5900v2Google Scholar
  14. 14.
    Fleurant, C., Ferland, J.A.: Genetic and hybrid algorithms for graph coloring. Annals of Operations Research 63, 437–461 (1996)CrossRefGoogle Scholar
  15. 15.
    Goldberg, D.E., Deb, K., Horn, J.: Massively multimodality, deception and genetic algorithms. Parallel Problem Solving from Nature 2, 37–46 (1992)Google Scholar
  16. 16.
    Jelasity, M.: A wave analysis of the subset sum problem. In: Proceedings of the Seventh International Conference on Genetic Algorithms, San Francisco, CA, pp. 89–96 (1997)Google Scholar
  17. 17.
    Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation 12(3), 273–302 (2004)CrossRefGoogle Scholar
  18. 18.
    Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing: A genetic algorithm. Parallel Computing 21, 1–28 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Martin, O.C., Otto, S.W., Felten, E.W.: Large-step markov chains for the TSP: Incorporating local search heuristics. Operation Research Letters 11, 219–224 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Salto, C., Alba, E.: Designing Heterogeneous Distributed GAs by Efficient Self-Adapting the Migration Period. Applied Intelligence (2011), doi:10.1007/s10489-011-0297-9Google Scholar
  21. 21.
    Salto, C., Alba, E., Luna, F.: Using Landscape Measures for the Online Tuning of Heterogeneous Distributed GAs. In: Proceedings of the GECCO 2011, pp. 691–694 (2011)Google Scholar
  22. 22.
    Syswerda, G.: A study of reproduction in generational and steady-state genetic algorithms. In: Foundations of Genetic Algorithms, pp. 94–101. Morgan Kauffman (1991)Google Scholar
  23. 23.
    Talbi, E.-G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8(5), 541–564 (2002)CrossRefGoogle Scholar
  24. 24.
    Talbi, E.-G., Muntean, T., Samarandache, I.: Hybridation des algorithmes génétiques aveq la recherche tabou. In: Evolution Artificielle, EA 1994 (1994)Google Scholar
  25. 25.
    Voigt, H.-M., Born, J., Santibanez-Koref, I.: Modeling and simulation of distributed evolutionary search processes for function optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 373–380. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  26. 26.
    Yao, X.: A new Simulated Annealing Algorithm. International Journal of Computer Mathematics 56, 161–168 (1995)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Universidad de MálagaMĺagaSpain

Personalised recommendations