Journal of Heuristics

, Volume 19, Issue 6, pp 881–915 | Cite as

ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

  • J. Humeau
  • A. Liefooghe
  • E. -G. Talbi
  • S. Verel


This paper presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search metaheuristics: ParadisEO-MO. A substantial number of single solution-based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.


Local search Metaheuristic Fitness landscapes  Conceptual unified model Algorithm design and analysis Software framework  



The authors would like to gratefully acknowledge the reviewers for their valuable feedback that highly contributed to improve the quality of the paper. Moreover, we would like to thank the Inria research institute for their support on the DOLPHIN project. Thanks are also due to all the members of the DOLPHIN research group for their collaboration in the development of the ParadisEO framework.


  1. Aarts, E.H.L., Lenstra, J.K.: Local Search in Combinatorial Optimization. Wiley, New York (1997)zbMATHGoogle Scholar
  2. Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Oper. Res. 54(1), 99–114 (2006)CrossRefzbMATHGoogle Scholar
  3. Alba, E., Almeida, F., Blesa, M., Cotta, C., Díaz, M., Dorta, I., Gabarró, J., González, J., León, C., Moreno, L., Petit, J., Roda, J., Rojas, A., Xhafa, F., (2002) MALLBA: A library of skeletons for combinatorial optimisation. In: Parallel Processing Conference (Euro-Par, 2002). Lecture Notes in Computer Science, vol. 2400, pp. 927–932. Springer, Berlin (2002)Google Scholar
  4. Altenberg, L.: Fitness distance correlation analysis: an instructive counterexemple. In: Bäck T (ed.) Seventh International Conference on Genetic Algorithms, pp. 57–64. Morgan Kaufmann, San Francisco (1997)Google Scholar
  5. Bastolla, U., Porto, M., Roman, H.E., Vendruscolo, M.: Statiscal properties of neutral evolution. J. Mol. Evol. 57(S), 103–119 (2003)CrossRefGoogle Scholar
  6. Benoist, T., Estellon, B., Gardi, F., Megel, R., Nouioua, K.: LocalSolver 1.x: a black-box local-search solver for 0–1 programming. Q. J. Oper. Res. 9, 299–316 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  7. Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 11–18. Morgan Kaufmann Publishers Inc., San Francisco, GECCO ’02 (2002)Google Scholar
  8. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA—a platform and programming language independent interface for search algorithms. In: Second International Conference on Evolutionary Multi-Criterion Optimization (EMO 2003), pp. 494–508. Faro (2003).Google Scholar
  9. Boisson, J.C., Jourdan, L., Talbi, E.G.: Metaheuristics based de novo protein sequencing: a new approach. Appl. Soft Comput. 11(2), 2271–2278 (2011)CrossRefGoogle Scholar
  10. Burke, E., Newall, J.: (2002) Enhancing timetable solutions with local search methods. In: Practise and Theory of Automated Timetabling IV (PATAT 2002, Gent, Belgium). Lecture Notes in Computer Science, vol. 2740, pp. 195–206. IEEE Press, Springer (2002)Google Scholar
  11. Cahon, S., Melab, N., Talbi, E.G.: ParadisEO: a framework for the reusable design of parallel and distributed metaheuristics. J. Heuristics 10(3), 357–380 (2004)Google Scholar
  12. Cerny, V.: A thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45, 41–51 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  13. Charon, I., Hudry, O.: The noising method: a new method for combinatorial optimization. Oper. Res. Lett. 14, 133–137 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  14. Clergue, M., Collard, P.: GA-hard functions built by combination of trap functions. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 249–254. IEEE Press (2002)Google Scholar
  15. Daolio, F., Verel, S., Ochoa, G., Tomassini, M.: Local optima networks of the quadratic assignment problem. In: Proceeding of IEEE world conference on computational intelligence (WCCI), pp. 3145-3152. Barcelona, Spain (2010)Google Scholar
  16. Dekkers, A., Aarts, E.: Global optimization and simulated annealing. Math. Program. 50, 367–393 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  17. Di Gaspero, L., Roli, A., Schaerf, A.: Easyanalyzer: an object-oriented framework for the experimental analysis of stochastic local search algorithms. In: International Conference on Engineering Stochastic Local Search Algorithms SLS: Springer, pp. 76–90. Heidelberg. Lecture Notes in Computer Science, Berlin (2007)Google Scholar
  18. Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E. Parameter control in evolutionary algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence, vol. 54, pp. 19–46. Springer, Berlin (2007)Google Scholar
  19. Feo, T.A., Resende, M.G.C.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8, 67–71 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  20. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6, 109–133 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  21. Gaspero, L.D., Schaerf, A.: EasyLocal++: an object-oriented framework for flexible design of local search algorithms. Softw. Pract. Experience 33(8), 733–765 (2003)CrossRefGoogle Scholar
  22. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  23. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Dordrecht (1997)CrossRefzbMATHGoogle Scholar
  24. Glover, F., Millan, C.M.: The general employee scheduling problem: an integration of MS and AI. Comput. Oper. Res. 13(5), 563–573 (1986)CrossRefGoogle Scholar
  25. Gu, J., Huang, X.: Efficient local search with search space smoothing: a case study of the traveling salesman problem. IEEE Trans. Syst. Man Cybern. 24(5), 728–735 (1994)CrossRefGoogle Scholar
  26. Halim, S., Yap, R.H.C., Lau, H.C.: An integrated white+black box approach for designing and tuning stochastic local search. In: 13th International Conference on Principles and Practice of Constraint Programming (CP 2007). Lecture Notes in Computer Science, vol. 4741, pp. 332–347. Springer, Berlin (2007)Google Scholar
  27. Hansen, P.: The Steepest Ascent Mildest Descent Heuristic for Combinatorial Programming, Congress on Numerical Methods in Combinatorial Optimization. Capri (1986)Google Scholar
  28. Hart, J.P., Shogan, A.W.: Semi-greedy heuristics: an empirical study. Oper. Res. Lett. 6(3), 107–114 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  29. Hoos, H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2004)Google Scholar
  30. Hoos, H.H.: Programming by optimization. Commun. ACM 55(2), 70–80 (2012)Google Scholar
  31. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Int. Res. 36(1), 267–306 (2009)zbMATHGoogle Scholar
  32. Johnson, D.S.: Local optimization and the travelling salesman problem. In: 17th Colloquium on Automata, Languages and Programming. Lecture Notes in Computer Science vol. 443, pp. 446–461. Springer, Berlin (1990)Google Scholar
  33. Jones, M.: A object-oriented framework for the implementation of search techniques. Ph.D. Thesis, University of East Anglia (2000)Google Scholar
  34. Jones, M., McKeown, G., Rayward-Smith, V.: Templar: a object-oriented framework for distributed combinatorial optimization. In: Proceedings of the UNICOM Seminar on Modern Heuristics for Decision Support. UNICOM Ltd, Brunel university (1998)Google Scholar
  35. Jones, T.: Evolutionary algorithms, fitness landscapes and search. Ph.D. Thesis, University of New Mexico, Albuquerque (1995)Google Scholar
  36. Keijzer, M., Merelo, J.J., Romero, G., Schoenauer, M.: Evolving objects: a general purpose evolutionary computation library. In: 5th International Conference on Artificial Evolution (EA 2001), pp. 231–244. Le Creusot, France (2001)Google Scholar
  37. Khanafer, A., Clautiaux, F., Hanafi, S., El-Ghazali, T.: The min-conflict packing problem. Comput. Oper. Res. 39, 2122–2132 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  38. Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge (1983)CrossRefGoogle Scholar
  39. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  40. Krasnogor, N., Smith, J.:MAFRA: A Java memetic algorithms framework. In: Data Mining with Evolutionary Algorithms, pp. 125-131. Las Vegas (2000)Google Scholar
  41. Lecron, F., Manneback, P., Tuyttens, D.: Exploiting grid computation for solving the vehicle routing problem. In: 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pp. 1–6 (2010)Google Scholar
  42. Liefooghe, A., Jourdan, L., Talbi, E.G.: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO. Eur. J. Oper. Res. 209(2), 104–112 (2011)MathSciNetCrossRefGoogle Scholar
  43. Liefooghe, A., Humeau, J., Mesmoudi, S., Jourdan, L., Talbi, E.G.: On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. J. Heuristics 18(2), 317–352 (2012)CrossRefGoogle Scholar
  44. Locatelli, M.: Simulated annealing algorithms for continuous global optimization: convergence conditions. J. Optim. Theory Appl. 29(1), 87–102 (2000)Google Scholar
  45. Lourenco, H.R., Martin, O., Stutzle, T.: Handbook of Metaheuristics, Operations Research and Management Science, vol. 57, pp. 321–353. Kluwer Academic Publishers, chap Iterated local search (2002)Google Scholar
  46. Lukasiewycz, M., Glaß, M., Reimann, F., Teich, J.: Opt4J—a modular framework for meta-heuristic optimization. In: Proceedings of the Genetic and Evolutionary Computing Conference (GECCO 2011). Dublin (2011)Google Scholar
  47. Madras, N.: Lectures on Monte Carlo Methods. American Mathematical Society, Providence (2002)zbMATHGoogle Scholar
  48. Marmion, M.E., Dhaenens, C., Jourdan, L., Liefooghe, A., Verel, S.: NILS: a Neutrality-based Iterated Local Search and its application to Flowshop Scheduling. In: Merz, P., Hao, J.K. (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, vol. 6622, pp. 191–202. Springer, Turino (2011a)Google Scholar
  49. Marmion, M.E., Dhaenens, C., Jourdan, L., Liefooghe, A., Verel, S.: On the neutrality of flowshop scheduling fitness landscapes. In: 5th Learning and Intelligent OptimizatioN Conference (LION 5). Lecture Notes in Computer Science, vol. 6683, pp. 238–252. Springer, Rome (2011b)Google Scholar
  50. Marmion, M.E., Mascia, F., López-Ibáñez, M., Stützle, T. (to appear): Automatic design of hybrid stochastic local search metaheuristics. Hybrid Metaheuristics (HM 2013). Lecture Notes in Computer Science. Springer, Berlin (2013)Google Scholar
  51. Martin, O., Otto, S., Felten, E.W.: Large-step markov chains for the traveling salesman problem. Complex Syst. 5(3), 299–326 (1991)MathSciNetzbMATHGoogle Scholar
  52. Melab, N., Luong, T.V., Karima, B., Talbi, E.G.: Towards ParadisEO-MO-GPU: a framework for GPU-based Local Search Metaheuristics. 11th International Work-Conference on Artificial Neural Networks, Torremolinos-Málaga, Espagne. Lecture Notes in Computer Science, vol. 6691. Springer (2011)Google Scholar
  53. Michel, L., Hentenryck, P.V.: Localizer++: an open library for local search. Technical Report CS-01-02. Brown University, Computer Science (2001)Google Scholar
  54. Michel, L., See, A., Hentenryck, P.V.: Parallel and distributed local search in COMET. Comput. Oper. Res. 36(8), 2357–2375 (2009)CrossRefzbMATHGoogle Scholar
  55. Mladenovic, M., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  56. Nannen, V., Eiben, A.E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 975–980. Morgan Kaufmann Publishers Inc., San Francisco, IJCAI’07 (2007)Google Scholar
  57. Ochoa, G., Tomassini, M., Verel, S., Darabos, C.: A study of NK landscapes’ basins and local optima networks. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 555–562. ACM, New York (2008)Google Scholar
  58. Ochoa, G., Verel, S., Tomassini, M. First-improvement vs. best-improvement local optima networks of nk landscapes. In: Proceedings of the 11th International Conference on Parallel Problem Solving From Nature, Krakow, Poland, pp. 104–113.Google Scholar
  59. Ozdamar, L., Demirhan, M.: Experiments with new stochastic global optimization search techniques. Comput. Oper. Res. 27(9), 841–865 (2000)CrossRefGoogle Scholar
  60. Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization Algorithms and Complexity. Prentice-Hall, Inc., Englewood Cliffs (1982)zbMATHGoogle Scholar
  61. Parejo, J.A., Ruiz-Cortés, A., Lozano, S., Fernández, P.: Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput. 16(3), 527–561 (2012)CrossRefGoogle Scholar
  62. Quick, R., Rayward-Smith, V., Smith, G.: Fitness distance correlation and ridge functions. In: Fifth Conference on Parallel Problems Solving from Nature (PPSN’98). Lecture Notes in Computer Science, vol. 1498, pp. 77–86. Springer, Heidelberg (1998)Google Scholar
  63. Reidys, C.M., Stadler, P.F.: Neutrality in fitness landscapes. Appl. Math. Comput. 117(2–3), 321–350 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  64. Rodriguez-Tello, E., Hao, J.K., Torres-Jimenez, J.: An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem. Comput. Oper. Res. 35(10), 3331–3346 (2008)CrossRefzbMATHGoogle Scholar
  65. Rosé, H., Ebeling, W., Asselmeyer, T.: The density of states—a measure of the difficulty of optimisation problems. Parallel Problem Solving from Nature (PPSN 1996), pp. 208–217 (1996)Google Scholar
  66. Rothlauf, F.: Representations for genetic and evolutionary algorithms, 2nd edn. Springer, Berlin (2006)Google Scholar
  67. Sendhoff, B., Kreutz, M., von Seelen, W.: A condition for the genotype-phenotype mapping: causality. In: Proceedings of the 7th International Conference on Genetic Algorithms, pp. 73–80. East Lansing (1997)Google Scholar
  68. Stadler, P.F.: Fitness landscapes. In: Biological Evolution and Statistical Physics. Lecture Notes Physics, vol. 585, pp. 187–207. Springer, Heidelberg (2002)Google Scholar
  69. Stutzle, T.: Local search algorithms for combinatorial problems—analysis, algorithms and new applications. Ph.D. Thesis, DISKI—Dissertationen zur Kunstliken Intelligenz., Sankt augustin (1999)Google Scholar
  70. Talbi, E.G.: Metaheuristics from Design to Implementation. Wiley, Chichester (2009)zbMATHGoogle Scholar
  71. Talbi, E.G., Hafidi, Z., Geib, J.M.: A parallel adaptive tabu search approach. Parallel comput. 24(14), 2003–2019 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  72. Van Nimwegen, E., Crutchfield, J., Huynen, M.: Neutral evolution of mutational robustness. Proc. Nat. Acad. Sci. USA 96, 9716–9720 (1999)CrossRefGoogle Scholar
  73. Verel, S.: Fitness landscapes and graphs: multimodularity, ruggedness and neutrality. In: 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO), pp. 3593–3656. ACM, Montreal (2009)Google Scholar
  74. Verel, S., Collard, P., Clergue, M.: Where are bottleneck in NK fitness landscapes? In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), pp. 273–280. IEEE Press, Canberra (2003)Google Scholar
  75. Voss, S., Woodruff, D.L.: Optimization software class librairies. Kluwer, Boston (2002)Google Scholar
  76. Voudouris, C.: Guided local search—an illustrative example in function optimization. BT Technol. J. 16(3), 46–50 (1998)CrossRefGoogle Scholar
  77. Voudouris, C., Tsang, E.: Guided local search. Eur. J. Oper. Res. 113(2), 469–499 (1999)CrossRefzbMATHGoogle Scholar
  78. Weinberger, E.D.: Correlated and uncorrelatated fitness landscapes and how to tell the difference. Biol. Cybern. 63, 325–336 (1990)CrossRefzbMATHGoogle Scholar
  79. Weinberger, E.D.: Local properties of Kauffman’s NK model, a tuneably rugged energy landscape. Phys. Rev. A 44(10), 6399–6413 (1991)CrossRefGoogle Scholar
  80. White, D.R.: Software review: the ECJ toolkit. Genet. Program. Evolv. Mach. 13(1), 65–67 (2012)CrossRefGoogle Scholar
  81. Wilke, C.O.: Adaptative evolution on neutral networks. Bull. Math. Biol. 63, 715–730 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • J. Humeau
    • 1
  • A. Liefooghe
    • 2
    • 3
  • E. -G. Talbi
    • 2
    • 3
  • S. Verel
    • 2
    • 4
  1. 1.Département IAÉcole des Mines de DouaiDouaiFrance
  2. 2.Inria Lille-Nord EuropeDOLPHIN Research TeamVilleneuve d’AscqFrance
  3. 3.Laboratoire LIFL, UMR CNRS 8022Université Lille 1Villeneuve d’Ascq CedexFrance
  4. 4.Laboratoire I3S, UMR CNRS 6070Université Nice Sophia AntipolisSophia Antipolis CedexFrance

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