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Parallel hyperheuristics for the frequency assignment problem

Special issue on nature inspired cooperative strategies for optimization

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Abstract

This work presents a set of approaches used to deal with the frequency assignment problem (FAP), which is one of the key issues in the design of GSM networks. The used formulation of FAP is focused on aspects which are relevant for real-world GSM networks. A memetic algorithm, together with the specifically designed local search and variation operators, are presented. The memetic algorithm obtains good quality solutions but it must be adapted for each instance to be solved. A parallel hyperheuristic-based model was used to parallelize the approach and to avoid the requirement of the adaptation step of the memetic algorithm. The model is a hybrid algorithm which combines a parallel island-based scheme with a hyperheuristic approach. The main operation of the island-based model is kept, but the configurations of the memetic algorithms executed on each island are dynamically mapped. The model grants more computational resources to those configurations that show a more promising behavior. For this purpose two different criteria have been used in order to select the configurations. The first one is based on the improvements that each configuration is able to achieve along the executions. The second one tries to detect synergies among the configurations, i.e., detect which configurations obtain better solutions when they are cooperating. Computational results obtained for two different real-world instances of the FAP demonstrate the validity of the proposed model. The new designed schemes have made possible to improve the previously known best frequency plans for a real-world network.

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References

  1. Aardal KI, van Hoesel SPM, Koster AMCA, Mannino C, Sassano A (2007) Models and solution techniques for frequency assignment problems. Ann Oper Res 153(1): 79–129

    Article  MATH  MathSciNet  Google Scholar 

  2. Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley-Interscience, London

    Book  MATH  Google Scholar 

  3. Amaldi E, Capone A, Malucelli F, Mannino C (2006) Handbook of optimization in telecommunications, chap optimization problems and models for planning cellular networks. Springer, Berlin, pp 917–939

  4. Araya I, Neveu B, Riff MC (2008) An efficient hyperheuristic for strip-packing problems. In: Cotta C, Sörensen K (eds) Adaptive and multilevel metaheuristics,studies in computational intelligence vol 136. Springer, Berlin, pp 61–76

    Chapter  Google Scholar 

  5. Avenali A, Mannino C, Sassano A (2002) Minimizing the span of d-walks to compute optimum frequency assignments. Math Program A 91: 357–374

    Article  MATH  MathSciNet  Google Scholar 

  6. Bader-El-Den MB, Poli R, Fatima S (2009) Evolving timetabling heuristics using a grammar-based genetic programming hyper- heuristic framework. Memetic Comput 1(3): 205–219

    Article  Google Scholar 

  7. Bjorklund P, Varbrand P, Yuan D (2005) Optimized planning of frequency hopping in cellular networks. Comput Oper Res 32(1): 169–186

    Article  MathSciNet  Google Scholar 

  8. Burke E, Kendall G, Silva JL, O’Brien R, Soubeiga E (2005) An Ant algorithm hyperheuristic for the project presentation scheduling problem. In: Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), vol 3. Edinburgh, Scotland, pp 2263–2270

  9. Burke EK, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Handbook of Meta-heuristics. Hyper-heuristics: an emerging direction in modern search technology. Kluwer, Dordrecht

    Google Scholar 

  10. Burke EK, Kendall G, Soubeiga E (2003) A Tabu-search hyperheuristic for timetabling and rostering. J. Heuristics 9(6): 451–470

    Article  Google Scholar 

  11. Burke EK, McCollum B, Meisels A, Petrovic S, Qu R (2007) A graph-based hyper-heuristic for educational timetabling problems. Eur J Oper Res 176(1): 177–192

    Article  MATH  MathSciNet  Google Scholar 

  12. Cantú-Paz E (1998) A survey of parallel genetic algorithms. Calc Paralleles 10

  13. Chen PC, Kendall G, Vanden Berghe G (2007) An ant based hyper-heuristic for the travelling tournament problem. In: Proceedings of IEEE symposium of computational intelligence in scheduling (CISched 2007). Honolulu, Hawaii, pp 19–26

  14. Cowling P, Kendall G, Han L (2002) An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of the 2002 IEEE congress on evolutionary computation (CEC 2002). IEEE Computer Society, Honolulu Hawaii, pp 1185–1190

  15. Cowling P, Kendall G, Soubeiga E (2001) A parameter-free hyperheuristic for scheduling a sales summit. In: Proceedings of 4th metahuristics international conference (MIC 2001). Porto Portugal, pp 127–131

  16. Cowling PI, Kendall G, Soubeiga E (2002) Hyperheuristics: a robust optimisation method applied to nurse scheduling. In: Guervós JJM, Adamidis P, Beyer HG, Martín JLFV, Schwefel HP (eds) PPSN lecture notes in computer science, vol 2439. Springer, Berlin, pp 851–860

    Google Scholar 

  17. Dems̆ar J (2006) Statistical comparison of classifiers over multiple data sets. J Machine Learn Res 7: 1–30

    MathSciNet  Google Scholar 

  18. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1): 29–41

    Article  Google Scholar 

  19. Dowsland K, Soubeiga E, Burke E (2007) A simulated annealing hyper-heuristic for determining shipper sizes. Eur J Oper Res 179(3): 759–774

    Article  MATH  Google Scholar 

  20. Eisenblätter A (2001) Frequency assignment in GSM networks: Models, heuristics, and lower bounds. Ph.D. thesis, Technische Universität Berlin

  21. Eisenblätter A, Grötschel M, Koster AMCA (2002) Frequency planning and ramifications of coloring. Discuss Math Graph Theory 22(1): 51–88

    MATH  MathSciNet  Google Scholar 

  22. Fischetti M, Lepsch C, Minerva G, Romanin-Jacur G, Toto E (2000) Frequency assignment in mobile radio systems using branch-and-cut techniques. Eur J Oper Res 123(2): 241–255

    Article  MATH  Google Scholar 

  23. Furuskar A, Naslund J, Olofsson H (1999) EDGE—enhanced data rates for GSM and TDMA/136 evolution. Ericsson Rev (1)

  24. Garg P (2009) A comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm. Int J Netw Secur Appl 1(1): 34–42

    Google Scholar 

  25. Glover F (1998) A template for scatter search and path relinking. In: (eds) In: AE ’97: Selected papers from the third european conference on artificial evolution. Springer, London, pp 3–54

    Google Scholar 

  26. Granbohm H, Wiklund J (1999) GPRS—general packet radio service. Ericsson Rev (1)

  27. Gratch J, Chien S (1993) Learning search control knowledge for the deep space network scheduling problem. Tech. rep., Champaign, IL, USA

  28. Greff JY, Idoumghar L, Schott R (2004) Application of markov decision processes to the frequency assignment problem. Appl Artif Intell 18(8): 761–773

    Article  Google Scholar 

  29. Hale WK (1980) Frequency assignment: theory and applications. Proc IEEE 68(12): 1497–1514

    Article  Google Scholar 

  30. Hoos HH (1999) On the run-time behavior of stochastic local search algorithms for SAT. In: Proceedings of AAAI’99. MIT Press, pp 661–666

  31. Idoumghar L, Schott R (2006) A new hybrid GA-MDP algorithm for the frequency assignment problem. In: Proceedings of the 18th IEEE international conference on tools with artificial intelligence (ICTAI’06), pp 18–25

  32. Jaumard B, Marcotte O, Meyer C (1999) Telecommunications network planning, chap, mathematical models and exact methods for channel assignment in cellular networks. Kluwer, UK, pp 239–256

  33. Kendall G, Cowling P, Soubeiga E (2002) Choice function and random hyperheuristics. In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning (SEAL 2002). Singapore, pp 667–671

  34. Kendall G, Mohamad M (2004) Channel assignment in cellular communication using a great deluge hyper-heuristic. In: Proceedings of the 2004 IEEE international conference on networks (ICON). Singapore, pp 769–773

  35. Kendall G, Mohamad M (2004) Channel assignment optimisation using a hyper-heuristic. In: Proceedings of the 2004 IEEE conference on cybernetics and intelligent systems (CIS 2004). Singapore, pp 790–795 (2004)

  36. Kim SS, Smith AE, Lee JH (2007) A memetic algorithm for channel assignment in wireless FDMA systems. Comput Oper Res 34: 1842–1856

    Article  MATH  Google Scholar 

  37. Kuurne AMJ (2002) On GSM mobile measurement based interference matrix generation. In: IEEE 55th vehicular technology conference. VTC Spring 2002, pp 1965–1969

  38. Le MN, Ong YS, Jin Y, Sendhoff B (2009) Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memetic Comput 1(3): 175–190

    Article  Google Scholar 

  39. Leese R, Hurley S (2002) Methods and algorithms for radio channel assignment. Oxford lecture series in mathematics and its applications. Oxford University Press, New York

    Google Scholar 

  40. León C, Miranda G, Segredo E, Segura C (2008) Parallel hypervolume-guided hyperheuristic for adapting the multi-objective evolutionary island model. In: International workshop on nature inspired cooperative strategies for optimization, studies in computational intelligence. Springer, Berlin

  41. León C, Miranda G, Segura C (2007) Parallel skeleton for multi-objective optimization. In: Genetic and evolutionary computation conference. ACM, London, p 906

  42. Leon C, Miranda G, Segura C (2009) A memetic algorithm and a parallel hyperheuristic island-based model for a 2d packing problem. In: GECCO’09: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, New York, pp 1371–1378

  43. León C, Miranda G, Segura C (2009) METCO: a parallel plugin-based framework for multi-objective optimization. Int J Artif Intell Tools 18(4)

  44. Luna F, Blum C, Alba E, Nebro AJ (2007) ACO vs EAs for solving a real-world frequency assignment problem in GSM networks. In: Genetic and evolutionary computation conference (GECCO 2007), pp 94–101

  45. Luna F, Estébanez C, León C, Chaves-González JM, Alba E, Aler R, Segura C, Vega-Rodríguez MA, Nebro AJ, Valls JM, Miranda G, Gómez-Pulido JA (2008) Metaheuristics for solving a real-world frequency assignment problem in gsm networks. In: Conference on genetic and evolutionary computation (GECCO 2008), pp 1579–1586

  46. Luna F, Nebro AJ, Alba E, Durillo JJ (2008) Solving large-scale real-world telecommunication problems using a grid-based genetic algorithm. Eng Optim 40(11): 1067–1084

    Article  Google Scholar 

  47. Mannino C, Oriolo G, Ricci F, Chandran S (2007) The stable set problem and the thinness of a graph. Oper Res Lett 35(1): 1–9

    Article  MATH  MathSciNet  Google Scholar 

  48. Mannino C, Sassano A (2003) An enumerative algorithm for the frequency assignment problem. Discrete Appl Math 129: 155–169

    Article  MATH  MathSciNet  Google Scholar 

  49. Matsui S, Watanabe I, Tokoro KI (2005) Application of the parameter-free genetic algorithm to the fixed channel assignment problem. Syst Comput Jpn 36(4): 71–81

    Article  Google Scholar 

  50. Metzger BH (1970) Spectrum management technique. In: 38th national ORSA meeting

  51. Mouly M, Paulet MB (1992) The GSM system for mobile communications. Mouly et Paulet, Palaiseau

    Google Scholar 

  52. Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B 36(1): 141–152

    Article  Google Scholar 

  53. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  54. Rapeli J (1995) UMTS: targets, system concept, and standardization in a global framework. IEEE Pers Commun 2(1): 30–37

    Article  Google Scholar 

  55. Salcedo-Sanz S, Bousoño-Calzón C (2005) A portable and scalable algorithm for a class of constrained combinatorial optimization problems. Comput Oper Res 32: 2671–2687

    Article  MATH  MathSciNet  Google Scholar 

  56. Segura C, Cervantes A, Nebro AJ, Jaraz-Simn MD, Segredo E, Garca S, Luna F, Gmez-Pulido JA, Miranda G, Luque C, Alba E, Vega-Rodrguez MA, Len C, Galvfn I (2009) Optimizing the DFCN broadcast protocol with a parallel cooperative strategy of multi-objective evolutionary algorithms. In: Springer (eds) 5th international conference devoted to evolutionary multi-criterion optimization, vol 5467. Nantes, France, pp 305–319

    Chapter  Google Scholar 

  57. Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton

    Book  Google Scholar 

  58. Simon MK, Alouini MS (2005) Digital communication over fading channels: a unified approach to performance analysis. Wiley, London

    Google Scholar 

  59. Talbi EG (2006) Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing). Wiley-Interscience, London

    Google Scholar 

  60. Terashima-Marn H, Ross P (1999) Evolution of constraint satisfaction strategies in examination timetabling. In: Proceedings of the genetic and evolutionary computation conference (GECCO99). Morgan Kaufmann, pp 635–642

  61. Veldhuizen DAV, Zydallis JB, Lamont GB (2003) Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans Evol Comput 7(2): 144–173

    Article  Google Scholar 

  62. Vink T, Izzo D (2007) Learning the best combination of solvers in a distributed global optimization environment. In: Proceedings of advances in global optimization: methods and applications (AGO). Mykonos, Greece, pp 13–17

  63. Voudouris C, Tsang E (1999) Guided local search. Eur J Oper Res 113(2): 449–499

    Article  Google Scholar 

  64. Walke BH (2002) Mobile radio networks: networking, protocols and traffic performance. Wiley, London

    Google Scholar 

  65. Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1): 67–82

    Article  Google Scholar 

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Correspondence to Carlos Segura.

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Segura, C., Miranda, G. & León, C. Parallel hyperheuristics for the frequency assignment problem. Memetic Comp. 3, 33–49 (2011). https://doi.org/10.1007/s12293-010-0044-5

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