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A new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problem

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Abstract

Artificial bee colony (ABC) is a recently introduced algorithm that models the behavior of honey bee swarm to address a multiobjective version for ABC, named Multiobjective Artificial Bee Colony algorithm (MO-ABC). We describe the methodology and results obtained when applying the new MO-ABC metaheuristic, which was developed to solve a real-world frequency assignment problem (FAP) in GSM networks. A precise mathematical formulation for this problem was used, where the frequency plans are evaluated using accurate interference information taken from a real GSM network. In this paper, our work is divided into two stages: In the first one, we have accurately tuned the algorithm parameters. Then, in the second step, we have compared the MO-ABC with previous versions of distinct multiobjective algorithms already developed to the same instances of the problem. As we will see, results show that this approach is able to obtain reasonable frequency plans when solving a real-world FAP. In the results analysis, we consider as complementary metrics the hypervolume indicator to measure the quality of the solutions to this problem as well as the coverage relation information.

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References

  1. Aardal KI, van Hoesel CPM, Koster AMCA, Mannino C, Sassano A (2003) Models and solution techniques for the frequency assignment problem. 4OR 1(4):261–317. http://fap.zib.de

    Google Scholar 

  2. Arsuaga-Rios M, Vega-Rodriguez M, Prieto-Castrillo F (2011) Multi-objective artificial bee colony for scheduling in grid environments. In: IEEE symposium on swarm intelligence (SIS), 2011, pp 1–7

  3. Babu B, Gujarathi AM (2007) Multi-objective differential evolution (mode) algorithm for multi-objective optimization: parametric study on benchmark test problems. J Future Eng Technol 3(1):47–59

    Google Scholar 

  4. Babu B, Jehan M (2003) Differential evolution for multi-objective optimization. In: The 2003 congress on evolutionary computation, 2003, vol 4. CEC‘03, pp 2696–2703

  5. Chaves-González JM et al (2008) SS vs PBIL to solve a real-world frequency assignment problem in GSM networks. In: EvoWorkshops, Lecture Notes in Computer Science, vol 4974. Springer, Berlin, pp 21–30

  6. da Silva Maximiano M et al (2009) Multiobjective frequency assignment problem using the MO-VNS and MO-SVNS algorithms. In: World congress on nature and biologically inspired computing (NaBIC). IEEE, Coimbatore, pp 221–226

  7. da Silva Maximiano M et al (2009) Parameter analysis for differential evolution with pareto tournaments in a multiobjective frequency assignment problem. In: Corchado E, Yin H (eds) Intelligent data engineering and automated learning—IDEAL 2009, vol 5788. Lecture Notes in Computer Science. Springer, Berlin, pp 799–806

  8. da Silva Maximiano M et al (2010) Application of differential evolution to a multi-objective real-world frequency assignment problem. In: Hiot LM, Ong YS, Qing A, Lee CK (eds) Differential evolution in electromagnetics, adaptation learning and optimization, vol 4. Springer, Berlin, pp 155–176

  9. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  10. Eisenblätter A et al (2001) Frequency assignment in GSM networks: models, heuristics and lower bounds. Ph.D. thesis

  11. Eisenblätter A et al (2002) Frequency planning and ramifications of coloring. Discuss Math Graph Theory 22:51–58

    Google Scholar 

  12. FAP Website (2011) http://fap.zib.de/

  13. Fonseca CM et al (2006) An improved dimension-sweep algorithm for the hypervolume indicator. In: IEEE congress on evolutionary computation. Vancouver, pp 1157–1163

  14. Gamst A, Rave W (1982) On frequency assignment in mobile automatic telephone systems. In: IEEE global communication conference GLOBECOM82, Miami, pp 309–315

  15. Geiger MJ (2008) Randomised variable neighbourhood search for multi objective optimisation. CoRR

  16. Gitizadeh M, Khalilnezhad H, Hedayatzadeh R (2012) Tcsc allocation in power systems considering switching loss using moabc algorithm. Electr Eng 1–13. doi:10.1007/s00202-012-0242-x

  17. GSM World (2011) http://www.gsmworld.com/news/statistics/index.shtml

  18. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report TR06

  19. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  20. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  21. Kuurne A (2002) On GSM mobile measurement based interference matrix generation. In: IEEE 55th vehicular technology conference (VTC), vol 4, pp 1965–1969

  22. Leese R, Hurley S (eds) (2002) Methods and algorithms for radio channel assignment. In: Oxford lecture series in mathematics and its applications. Oxford University Press, Oxford

  23. Liang YC, Chen AHL, Tien CY (2009) Variable neighborhood search for multi-objective parallel machine scheduling problems. In: Proceedings of the 8th international conference on information and management sciences (IMS 2009), vol 16, pp 511–535

  24. Luna F, Alba E, Nebro AJ, Pedraza S (2007) Evolutionary algorithms for real-world instances of the automatic frequency planning problem in GSM networks. EvoCOP 4446/2007:108–120

  25. Luna F, Blum C, Alba E, Nebro AJ (2007) ACO vs EAs for solving a real-world frequency assignment problem in GSM networks. GECCO ‘07, pp 94–101

  26. 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: GECCO ’08—proceedings of the 10th annual conference on genetic and evolutionary computation. ACM, Atlanta, pp 1579–1586

  27. Mishra AR (2004) Fundamentals of cellular network planning and optimisation: 2g/2.5g/3g … evolution to 4g. chap. Radio network planning and optimisation. Wiley, Hoboken, pp 21–54

    Book  Google Scholar 

  28. Omkar S, Senthilnath J, Khandelwal R, Naik GN, Gopalakrishnan S (2011) Artificial bee colony (abc) for multi-objective design optimization of composite structures. Appl Soft Comput 11(1):489–499

    Article  Google Scholar 

  29. Pareto Multi Objective Optimization (2005) doi:10.1109/ISAP.2005.1599245

  30. Qing A (2009) Differential evolution: fundamentals and applications in electrical engineering. Wiley-IEEE Press, Hoboken

    Google Scholar 

  31. Raquel CR, Prospero C Naval J (2005) An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the 2005 conference on genetic and evolutionary computation, GECCO ‘05. ACM, New York, pp 257–264

  32. Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, Hoboken

    Google Scholar 

  33. Weicker N, Szabo G, Weicker K, Widmayer P (2003) Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Trans Evol Comput 7(2):189–203

    Article  Google Scholar 

  34. Zhang H, Zhu Y, Zou W, Yan X (2012) A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production. Appl Math Model 36(6):2578–2591

    Article  MATH  Google Scholar 

  35. Zhou G, Wang L, Xu Y, Wang S (2012) An effective artificial bee colony algorithm for multi-objective flexible job-shop scheduling problem. In: Huang DS, Gan Y, Gupta P, Gromiha M (eds) Advanced intelligent computing theories and applications. With aspects of artificial intelligence. Lecture Notes in Computer Science, vol 6839. Springer, Berlin, pp 1–8

  36. Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: PPSN V—proceedings of the 5th international conference on parallel problem solving from nature. Springer, Amsterdam, pp 292–304

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Acknowledgments

This work was partially funded by the Spanish Ministry of Science and Innovation and FEDER under the contract TIN2008-06491-C04-04 (the MSTAR project). Thanks also to the Polytechnic Institute of Leiria, for the economic support offered to Marisa Maximiano to make this research.

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Authors and Affiliations

  1. Department of Informatics Engineering. School of Technology and Management, Polytechnic Institute of Leiria, Campus 2—Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901, Leiria, Portugal

    Marisa da Silva Maximiano

  2. Department Technologies of Computers and Communications, University Extremadura, Escuela Politécnica, Campus Universitario s/n, 10003, Cáceres, Spain

    Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido & Juan M. Sánchez-Pérez

Authors
  1. Marisa da Silva Maximiano
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  2. Miguel A. Vega-Rodríguez
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  3. Juan A. Gómez-Pulido
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  4. Juan M. Sánchez-Pérez
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Correspondence to Marisa da Silva Maximiano.

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da Silva Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A. et al. A new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problem. Neural Comput & Applic 22, 1447–1459 (2013). https://doi.org/10.1007/s00521-012-1046-7

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