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Applied Intelligence

, Volume 41, Issue 1, pp 145–166 | Cite as

Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts

  • E. Osaba
  • F. Diaz
  • E. Onieva
Article

Abstract

In this paper, a new multiple population based meta-heuristic to solve combinatorial optimization problems is introduced. This meta-heuristic is called Golden Ball (GB), and it is based on soccer concepts. To prove the quality of our technique, we compare its results with the results obtained by two different Genetic Algorithms (GA), and two Distributed Genetic Algorithms (DGA) applied to two well-known routing problems, the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP). These outcomes demonstrate that our new meta-heuristic performs better than the other techniques in comparison. We explain the reasons of this improvement.

Keywords

Meta-heuristics Golden ball Distributed genetic algorithm Routing problems Combinatorial optimization Intelligent transportation systems 

Notes

Acknowledgement

This work is an extension of the two-page late-breaking abstract presented in the fifteenth annual conference on genetic and evolutionary computation (GECCO)[82]. In that short abstract we introduce a preliminary version of our technique in a very concise way.

References

  1. 1.
    Papadimitriou C (2012) The new faces of combinatorial optimization. In: Combinatorial Optimization. LNCS, vol 7422, pp 19–23 CrossRefGoogle Scholar
  2. 2.
    Korte B, Vygen J (2012) Combinatorial optimization: theory and algorithms, vol 21. Springer, Berlin Google Scholar
  3. 3.
    Lawler E, Lenstra J, Kan A, Shmoys D (1985) The traveling salesman problem: a guided tour of combinatorial optimization, vol 3. Wiley, New York zbMATHGoogle Scholar
  4. 4.
    Coffman EG, Bruno JL (1976) Computer and job-shop scheduling theory. Wiley, New York zbMATHGoogle Scholar
  5. 5.
    Lenstra J, Kan A (1981) Complexity of vehicle routing and scheduling problems. Networks 11(2):221–227 CrossRefGoogle Scholar
  6. 6.
    Mattos Ribeiro G, Laporte G (2012) An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem. Comput Oper Res 39(3):728–735 CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Xu Y, Qu R (2012) A hybrid scatter search meta-heuristic for delay-constrained multicast routing problems. Appl Intell 36(1):229–241 CrossRefGoogle Scholar
  8. 8.
    Onieva E, Naranjo J, Milanes V, Alonso J, Garcia R, Perez J (2011) Automatic lateral control for unmanned vehicles via genetic algorithms. Appl Soft Comput 11(1):1303–1309 CrossRefGoogle Scholar
  9. 9.
    Zheng YJ, Chen SY (2013) Cooperative particle swarm optimization for multiobjective transportation planning. Appl Intell 39(1):202–216 CrossRefGoogle Scholar
  10. 10.
    Kang MH, Choi HR, Kim HS, Park BJ (2012) Development of a maritime transportation planning support system for car carriers based on genetic algorithm. Appl Intell 36(3):585–604 CrossRefGoogle Scholar
  11. 11.
    Masoud H, Jalili S, Hasheminejad SMH (2013) Dynamic clustering using combinatorial particle swarm optimization. Appl Intell 38(3):289–314 CrossRefGoogle Scholar
  12. 12.
    Shin KS, Jeong YS, Jeong MK (2012) A two-leveled symbiotic evolutionary algorithm for clustering problems. Appl Intell 36(4):788–799 CrossRefGoogle Scholar
  13. 13.
    Harman M, McMinn P, de Souza JT, Yoo S (2012) Search based software engineering: techniques, taxonomy, tutorial. In: Empirical software engineering and verification, vol 7007. Springer, Berlin, pp 1–59 CrossRefGoogle Scholar
  14. 14.
    Gao J, Sun L, Gen M (2008) A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput Oper Res 35(9):2892–2907 CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Wang L, Zhou G, Xu Y, Wang S, Liu M (2012) An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Int J Adv Manuf Technol 60(1):303–315 CrossRefGoogle Scholar
  16. 16.
    Zhang R, Wu C (2012) Bottleneck machine identification method based on constraint transformation for job shop scheduling with genetic algorithm. Inf Sci 188(1):236–252 CrossRefzbMATHGoogle Scholar
  17. 17.
    Wang K, Zheng YJ (2012) A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design. Appl Intell 37(4):520–526 CrossRefGoogle Scholar
  18. 18.
    Rahmati SHA, Zandieh M, Yazdani M (2013) Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem. Int J Adv Manuf Technol 64(5–8):915–932 CrossRefGoogle Scholar
  19. 19.
    Kirkpatrick S, Gellat C, Vecchi M (1983) Optimization by simulated annealing. Science 220(4598):671–680 CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Torres-Jimenez J, Rodriguez-Tello E (2012) New bounds for binary covering arrays using simulated annealing. Inf Sci 185(1):137–152 CrossRefGoogle Scholar
  21. 21.
    Glover F (1989) Tabu search, Part I. ORSA J Comput 1(3):190–206 CrossRefzbMATHGoogle Scholar
  22. 22.
    Hedar AR, Ali AF (2012) Tabu search with multi-level neighborhood structures for high dimensional problems. Appl Intell 37(2):189–206 CrossRefGoogle Scholar
  23. 23.
    Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading zbMATHGoogle Scholar
  24. 24.
    De Jong K (1975) Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Michigan, USA Google Scholar
  25. 25.
    Shi K, Li L (2013) High performance genetic algorithm based text clustering using parts of speech and outlier elimination. Appl Intell 38(4):511–519 CrossRefGoogle Scholar
  26. 26.
    Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2):243–278 CrossRefzbMATHMathSciNetGoogle Scholar
  27. 27.
    Wu J, Abbas-Turki A, El Moudni A (2012) Cooperative driving: an ant colony system for autonomous intersection management. Appl Intell 37(2):207–222 CrossRefGoogle Scholar
  28. 28.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Techn rep TR06, Erciyes Univ. Press, Erciyes Google Scholar
  29. 29.
    Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2012) A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif Intell Rev 37(4):520–526 Google Scholar
  30. 30.
    Tsai PW, Pan JS, Liao BY, Chu SC (2009) Enhanced artificial bee colony optimization. Int J Innov Comput Inf Control 5(12):5081–5092 Google Scholar
  31. 31.
    El-Abd M (2010) A cooperative approach to the artificial bee colony algorithm. In: IEEE congress on evolutionary computation, pp 1–5 CrossRefGoogle Scholar
  32. 32.
    Banharnsakun A, Achalakul T, Sirinaovakul B (2010) Artificial bee colony algorithm on distributed environments. In: IEEE second world congress on nature and biologically inspired computing, pp 13–18 Google Scholar
  33. 33.
    Parpinelli RS, Benitez CMV, Lopes HS (2010) Parallel approaches for the artificial bee colony algorithm. In: Handbook of swarm intelligence. Springer, Berlin, pp 329–345 Google Scholar
  34. 34.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the IEEE sixth international symposium on micro machine and human science, pp 39–43 CrossRefGoogle Scholar
  35. 35.
    Langdon W, Poli R (2007) Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evol Comput 11(5):561–578 CrossRefGoogle Scholar
  36. 36.
    Hasanzadeh M, Meybodi MR, Ebadzadeh MM (2013) Adaptive cooperative particle swarm optimizer. Appl Intell 39(2):397–420 CrossRefGoogle Scholar
  37. 37.
    Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Evolutionary programming VII. Springer, Berlin, pp 601–610 CrossRefGoogle Scholar
  38. 38.
    Xu Y, Wang Q, Hu J (2008) An improved discrete particle swarm optimization based on cooperative swarms. In: IEEE international conference on web intelligence and intelligent agent technology, vol 2, pp 79–82 Google Scholar
  39. 39.
    Niu B, Zhu Y, He X, Wu H (2007) MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl Math Comput 185(2):1050–1062 CrossRefzbMATHGoogle Scholar
  40. 40.
    Chanj J, Chu SC, Roddick JF, Pan JS (2005) A parallel particle swarm optimization algorithm with communication strategies. J Inf Sci Eng 21(4):809–818 Google Scholar
  41. 41.
    Manderick B, Spiessens P (1989) Fine-grained parallel genetic algorithms. In: Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, San Mateo, pp 428–433 Google Scholar
  42. 42.
    Reeves CR (1993) Modern heuristic techniques for combinatorial problems. Wiley, New York zbMATHGoogle Scholar
  43. 43.
    Whitley D, Rana S, Heckendorn RB (1999) The island model genetic algorithm: on separability, population size and convergence. Int J Comput Inf Technol 7:33–48 Google Scholar
  44. 44.
    Li C, Yang S (2008) An island based hybrid evolutionary algorithm for optimization. In: Simulated evolution and learning. Springer, Berlin, pp 180–189 CrossRefGoogle Scholar
  45. 45.
    Calégari P, Guidec F, Kuonen P, Kobler D (1997) Parallel island-based genetic algorithm for radio network design. J Parallel Distrib Comput 47(1):86–90 CrossRefGoogle Scholar
  46. 46.
    Abbasian R, Mouhoub M (2013) A hierarchical parallel genetic approach for the graph coloring problem. Appl Intell 39(3):510–528 CrossRefGoogle Scholar
  47. 47.
    Cantú-Paz E (1998) A survey of parallel genetic algorithms. Calc Paralléles 10(2):141–171 Google Scholar
  48. 48.
    Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation. IEEE, New York, pp 4661–4667 Google Scholar
  49. 49.
    Wang Gj, Zhang YB, Chen JW (2011) A novel algorithm to solve the vehicle routing problem with time windows: imperialist competitive algorithm. Adv Inf Sci Serv Sci 3(5) Google Scholar
  50. 50.
    Yousefikhoshbakht M, Sedighpour M (2013) New imperialist competitive algorithm to solve the travelling salesman problem. Int J Comput Math 3(5):108–116 Google Scholar
  51. 51.
    Dai C, Chen W, Zhu Y (2006) Seeker optimization algorithm. In: International conference on computational intelligence and security. Springer, Berlin, pp 225–229 Google Scholar
  52. 52.
    Dai C, Chen W, Song Y, Zhu Y (2010) Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization. J Syst Eng Electron 21(2):300–311 Google Scholar
  53. 53.
    Dai C, Chen W, Zhu Y, Zhang X (2009) Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans Power Syst 24(3):1218–1231 Google Scholar
  54. 54.
    Dai C, Chen W, Zhu Y (2010) Seeker optimization algorithm for digital IIR filter design. IEEE Trans Ind Electron 57(5):1710–1718 CrossRefGoogle Scholar
  55. 55.
    Lin S (1965) Computer solutions of the traveling salesman problem. Bell Syst Tech J 44(10):2245–2269 CrossRefzbMATHGoogle Scholar
  56. 56.
    Davis L (1985) Applying adaptive algorithms to epistatic domains. In: Proceedings of the international joint conference on artificial intelligence, vol 1, pp 161–163 Google Scholar
  57. 57.
    Julstrom BA (1995) Very greedy crossover in a genetic algorithm for the traveling salesman problem. In: Proceedings of the ACM symposium on applied computing, pp 324–328 Google Scholar
  58. 58.
    Ochi LS, Vianna DS, Drummond L, Victor A (1998) A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet. Future Gener Comput Syst 14(5):285–292 CrossRefGoogle Scholar
  59. 59.
    Liefooghe A, Humeau J, Mesmoudi S, Jourdan L, Talbi E (2012) On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. J Heuristics 18(2):317–352 CrossRefGoogle Scholar
  60. 60.
    Casazza M, Ceselli A, Nunkesser M (2012) Efficient algorithms for the double traveling salesman problem with multiple stacks. Comput Oper Res 39(5):1044–1053 CrossRefzbMATHMathSciNetGoogle Scholar
  61. 61.
    Ray SS, Bandyopadhyay S, Pal SK (2007) Genetic operators for combinatorial optimization in tsp and microarray gene ordering. Appl Intell 26(3):183–195 CrossRefzbMATHGoogle Scholar
  62. 62.
    Laporte G (1992) The vehicle routing problem: an overview of exact and approximate algorithms. Eur J Oper Res 59(3):345–358 CrossRefzbMATHGoogle Scholar
  63. 63.
    Ngueveu S, Prins C, Wolfler Calvo R (2010) An effective memetic algorithm for the cumulative capacitated vehicle routing problem. Comput Oper Res 37(11):1877–1885 CrossRefzbMATHMathSciNetGoogle Scholar
  64. 64.
    Lee CY, Lee ZJ, Lin SW, Ying KC (2010) An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem. Appl Intell 32(1):88–95 CrossRefGoogle Scholar
  65. 65.
    Cordeau J, Maischberger M (2012) A parallel iterated tabu search heuristic for vehicle routing problems. Comput Oper Res 39(9):2033–2050 CrossRefGoogle Scholar
  66. 66.
    Reinelt G (1991) Tsplib—a traveling salesman problem library. ORSA J Comput 3(4):376–384 CrossRefzbMATHGoogle Scholar
  67. 67.
    Larranaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13(2):129–170 CrossRefGoogle Scholar
  68. 68.
    Cordeau J, Laporte G (2003) A tabu search heuristic for the static multi-vehicle dial-a-ride problem. Transp Res, Part B, Methodol 37(6):579–594 CrossRefGoogle Scholar
  69. 69.
    Breedam A (2001) Comparing descent heuristics and metaheuristics for the vehicle routing problem. Comput Oper Res 28(4):289–315 CrossRefzbMATHGoogle Scholar
  70. 70.
    Tarantilis C (2005) Solving the vehicle routing problem with adaptive memory programming methodology. Comput Oper Res 32(9):2309–2327 CrossRefzbMATHMathSciNetGoogle Scholar
  71. 71.
    Tang H, Miller-Hooks E (2005) A tabu search heuristic for the team orienteering problem. Comput Oper Res 32(6):1379–1407 CrossRefGoogle Scholar
  72. 72.
    Tarantilis C, Kiranoudis C (2007) A flexible adaptive memory-based algorithm for real-life transportation operations: two case studies from dairy and construction sector. Eur J Oper Res 179(3):806–822 CrossRefzbMATHGoogle Scholar
  73. 73.
    Bianchessi N, Righini G (2007) Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery. Comput Oper Res 34(2):578–594 CrossRefzbMATHGoogle Scholar
  74. 74.
    Osaba E, Onieva E, Carballedo R, Diaz F, Perallos A (2014) An adaptive multi-crossover population algorithm for solving routing problems. In: Nature inspired cooperative strategies for optimization. Springer, Berlin, pp 113–124 CrossRefGoogle Scholar
  75. 75.
    Alfa A, Heragu S, Chen M (1991) A 3-opt based simulated annealing algorithm for vehicle routing problems. Comput Ind Eng 21(1):635–639 CrossRefGoogle Scholar
  76. 76.
    Rocki K, Suda R (2012) Accelerating 2-opt and 3-opt local search using GPU in the travelling salesman problem. In: IEEE international conference on high performance computing and simulation, pp 489–495 Google Scholar
  77. 77.
    Toth P, Vigo D (1987) The vehicle routing problem, vol 9. Society for Industrial and Applied Mathematics, Philadelphia Google Scholar
  78. 78.
    Lee ZJ (2012) A hybrid approach for vehicle routing problem with time windows. Adv Intell Transp Syst 1(1):11–18 Google Scholar
  79. 79.
    Osaba E, Onieva E, Carballedo R, Diaz F, Perallos A, Zhang X (2013) A multi-crossover and adaptive island based population algorithm for solving routing problems. J Zhejiang Univ Sci C 14(11):815–821 CrossRefGoogle Scholar
  80. 80.
    Savelsbergh M (1992) The vehicle routing problem with time windows: minimizing route duration. ORSA J Comput 4(2):146–154 CrossRefzbMATHGoogle Scholar
  81. 81.
  82. 82.
    Osaba E, Diaz F, Onieva E (2013) A novel meta-heuristic based on soccer concepts to solve routing problems. In: Proceeding of the fifteenth annual conference companion on genetic and evolutionary computation conference companion. ACM, New York, pp 1743–1744 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Deusto Institute of Technology (DeustoTech)University of DeustoBilbaoSpain

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