Abstract
In this article we present Ethane, a parallel heterogeneous metaheuristic model specifically designed for its execution on heterogeneous hardware environments. With Ethane we propose a hybrid parallel search algorithm inspired in the structure of the chemical compound of the same name, implementing a heterogeneous island model based in the structure of the chemical bonds of the ethane compound. Here we also shape a schema for describing a complete family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in nature, HydroCM (HydroCarbon inspired Metaheuristics), 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 (genetic algorithm and simulated annealing in this study), all them collaborating to solve the same problem. In addition to the nice natural metaphor we will show that Ethane, 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, as well as can achieve a better exploration/exploitation balance during the search process.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aarts EHL, Verhoeven MGA (1997) Genetic local search for the traveling salesman problem. Handbook of evolutionary computation. Institute of Physics Publishing and Oxford University Press, Bristol, pp G9.5:1–7
Alba E (2002) Parallel evolutionary algorithms can achieve super-lineal performance. Inf Process Lett 82:7–13
Alba E (2005a) Metaheuristics and parallelism. Parallel metaheuristics: a new class of algorithms. Wiley, New York, pp 79–103
Alba E (2005b) Parallel heterogeneous metaheuristics. Parallel metaheuristics: a new class of algorithms. Wiley, New York, pp 395–422
Alba E, Dorronsoro B (2008) The state of the art in cellular evolutionary algorithms. Cellular genetic algorithms. Springer, New York, pp 21–34
Alba E, Troya JM (2001) Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Futur Gener Comput Syst 17:451–465
Alba E, Nebro AJ, Troya JM (2002) Heterogeneous computing and parallel genetic algorithms. J Parallel Distrib Comput 62:1362–1385
Alba E, Luna F, Nebro AJ, Troya JM (2004) Parallel heterogeneous genetic algorithms for continuous optimization. Parallel Comput 30(5–6):699–719
Branke J, Kamper A, Schmeck H (2004) Distribution of evolutionary algorithms in heterogeneous networks. In: Lecture notes in computer science, vol 3102. Springer, Heidelberg, pp 923–934
Cheeseman P, Kanefsky B, Taylor WM (1991) Where the really hard problems are. In: Proceedings of the international joint conferences on artificial intelligence IJCAI-91, Sydney, pp 331–337
Chen H, Flann NS (1994) Parallel simulated annealing and genetic algorithms: a space of hybrid methods. In: Third conference on parallel problem solving from nature, Jerusalem, pp 428–436
Crainic TG, Toulouse M (2003) Parallel strategies for meta-heuristics. Handbook of metaheuristics. Kluwer, Norwell, pp 474–513
De Falco I, Del Balio R, Tarantino E, Vaccaro R (1994) Improving search by incorporating evolution principles in parallel tabu search. In: International conference on machine learning, New Brunswick, pp 823–828
De Jong K, Potter M, Spears W (1997) Using problem generators to explore the effects of epistasis. In: Proceedings of the seventh international conference on genetic algorithms, San Francisco, pp 338–345
Domínguez J, Alba E (2011) Ethane: a heterogeneous parallel search algorithm for heterogeneous platforms. DECIE’11, doi:arXiv:1105.5900v2
Fleurant C, Ferland JA (1996) Genetic and hybrid algorithms for graph coloring. Ann Oper Res 63:437–461
Folino G, Pizzuti C, Spezzano G, Vanneschi L, Tomassini M (2003) Diversity analysis in cellular and multipopulation genetic programming. IEEE Congr Evol Comput (1):305–311
Garey MR, Johnson DS (1990) Computers and intractability; a guide to the theory of NP-completeness. W. H. Freeman, New York
Goldberg DE, Deb K, Horn J (1992) Massively multimodality, deception and genetic algorithms. Parallel Probl Solving Nat 2:37–46
Hoos HH, Sttzle T (2000) SATLIB: an online resource for research on SAT. In: Gent IP, Maaren Hv, Walsh T (eds) SAT 2000. IOS Press, Amsterdam, pp 283–292
Jelasity M (1997) A wave analysis of the subset sum problem. In: Proceedings of the seventh international conference on genetic algorithms, San Francisco, pp 89–96
Lozano M, Herrera F, Krasnogor N, Molina D (2004) real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12(3):273–302
Mahfoud SW, Goldberg DE (1995) Parallel recombinative simulated annealing: a genetic algorithm. Parallel Comput 21:1–28
Martin OC, Otto SW, Felten EW (1992) Large-step markov chains for the TSP: incorporating local search heuristics. Oper Res Lett 11:219–224
Salto C, Alba E (2012) Designing heterogeneous distributed GAs by efficient self-adapting the migration period. Appl Intell 36:800–808
Salto C, Alba E, Luna F (2011) Using landscape measures for the online tuning of heterogeneous distributed GAs. In: Proceedings of the GECCO’11, Dublin, pp 691–694
Syswerda G (1991) A study of reproduction in generational and steady-state genetic algorithms. Foundations of genetic algorithms. Morgan Kauffman, San Mateo, pp 94–101
Talbi E-G (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564
Talbi E-G, Muntean T, Samarandache I (1994) Hybridation des algorithmes génétiques aveq la recherche tabou. In: Evolution Artificielle (EA94), Toulouse
Voigt H-M, Born J, Santibanez-Koref I (1990) Modeling and simulation of distributed evolutionary search processes for function optimization. In: PPPSN. Lecture notes in computer science, vol 496. Springer, Heidelberg, pp 373–380
Yao X (1995) A new simulated annealing algorithm. Int J Comput Math 56:161–168
Acknowledgments
Authors acknowledge funds from the Spanish Ministry MICINN and FEDER under contracts TIN2011-28194 (roadME) and TIN2008-06491-C04-01 (M* http://mstar.lcc.uma.es) and CICE, Junta de Andalucía, under contract P07-TIC-03044 (DIRICOM http://diricom.lcc.uma.es).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Domínguez, J., Alba, E. Dealing with hardware heterogeneity: a new parallel search model. Nat Comput 12, 179–193 (2013). https://doi.org/10.1007/s11047-012-9360-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11047-012-9360-7