Journal of Heuristics

, Volume 14, Issue 4, pp 313–333

Pareto memetic algorithm for multiple objective optimization with an industrial application

  • Arnaud Zinflou
  • Caroline Gagné
  • Marc Gravel
  • Wilson L. Price
Article

Abstract

Multiple objective combinatorial optimization problems are difficult to solve and often, exact algorithms are unable to produce optimal solutions. The development of multiple objective heuristics was inspired by the need to quickly produce acceptable solutions. In this paper, we present a new multiple objective Pareto memetic algorithm called PMSMO. The PMSMO algorithm incorporates an enhanced fine-grained fitness assignment, a double level archiving process and a local search procedure to improve performance. The performance of PMSMO is benchmarked against state-of-the-art algorithms using 0–1 multi-dimensional multiple objective knapsack problem from the literature and an industrial scheduling problem from the aluminum industry.

Keywords

Memetic algorithms Multiple objectives Combinatorial optimization Niche Elitism 

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References

  1. Coello Coello, A.C., Pulido, G.T.: Multiobjective optimization using a micro-genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), pp. 274–282, San Francisco, California, 2001 Google Scholar
  2. Czyzak, P., Jaszkiewicz, A.: Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J. Multi-Criteria Dec. Anal. 7, 34–47 (1998) MATHCrossRefGoogle Scholar
  3. Deb, K.: A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: NSGA II. In: Parallel problem Solving form Nature—PPSN VI. Springer Lecture Notes in Computer Science, pp. 849–858, 2000 Google Scholar
  4. Deb, K., Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Proceedings of Evolutionary Multi-Criterion Optimization, pp. 67–81, 2001 Google Scholar
  5. Fonseca, C.M., Fleming, P.J.: Genetic algorithm for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms San Mateo, California, pp. 416–423, 1993 Google Scholar
  6. Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3(1), 1–16 (1995) CrossRefGoogle Scholar
  7. Gagné, C., Gravel, M., Price, W.L.: Algorithme d’optimisation par colonie de fourmis avec matrices de visibilité multiples pour la résolution d’un problème d’ordonnancement industriel. Inform. Syst. Oper. Res. (INFOR) 40(2), 259–276 (2002) Google Scholar
  8. Gandibleux, X., Mezdaoui, N., Fréville, A.: A tabu search procedure to solve multiobjective combinatorial optimization problems. In: Caballero, R., Steuer, R. (eds.) Proceeding volume of MOPGP ’96. Springer, Berlin (1996) Google Scholar
  9. Garey, M.S., Johnson, D.S.: Computer and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979) Google Scholar
  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, Reading (1989) MATHGoogle Scholar
  11. Goldberg, D.E., Lingle, R.: Alleles, Loci and the Traveling Salesman Problem. In: Proceeding of the first Int. Conf. on Genetic Algorithms (ICGA’85), pp. 154–159. Carnegie-Mellon University, Pittsburgh (1985) Google Scholar
  12. Gravel, M., Price, W.L., Gagné, C.: Scheduling jobs in an Alcan aluminum foundry using a genetic algorithm. Int. J. Prod. Res. 38(2), 309–322 (2000) MATHCrossRefGoogle Scholar
  13. Gravel, M., Price, W.L., Gagné, C.: Scheduling continuous casting of aluminum using a multiple-objective ant colony optimization metaheuristic. Eur. J. Oper. Res. 143(1), 218–229 (2002) MATHCrossRefGoogle Scholar
  14. Hajela, P., Lin, C.-Y.: Genetic search strategies in multicriterion optimal design. Struct. Optim. 4, 99–107 (1992) CrossRefGoogle Scholar
  15. Hansen, M.P.: Tabu search for multiple objective combinatorial optimization: MOTS. In: The 13th International Conference on Multiple Criteria Decision Making (MCDM), University of Cape Town, South Africa. Springer, Berlin (1997) Google Scholar
  16. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceeding of the first IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, vol. 1, pp. 82–87, Piscataway, NJ, 1994 Google Scholar
  17. Iredi, S., Merkle, D., Middendorf, M.: Bi-criterion optimization with multi colony ant algorithms. In: Zitzler, E., et al. (eds.) Evolutionary Multi-Criterion Optimization, First International Conference (EMO’01). LNCS, vol. 1993, pp. 359–372. Springer, Zurich (2001) Google Scholar
  18. Ishibuchi, H., Murata, T.: Multi-objective genetic local search algorithm. In: Proceedings of the 1996 International Conference on Evolutionary Computation, pp. 119–124, Nagoya, Japan, 1996 Google Scholar
  19. Johnson, D.S., McGeoch, L.A.: The traveling salesman problem: a case study in local optimization. In: Aarts, E.H.L., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization. Wiley, New York (1997) Google Scholar
  20. Knowles, J.D., Corne, D.W.: M-PAES: a memetic algorithm for multiobjective optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 325–332, 2000a. Google Scholar
  21. Knowles, J.D., Corne, D.W.: The Pareto-envelope based selection algorithm for multiobjective optimization. In: Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI), pp. 839–848, Berlin, 2000b Google Scholar
  22. Or, I.: Traveling salesman-type combinatorial problems and their relation to the logistics of regional blood banking. Ph.D. thesis, Northwestern University, Evanston, Illinois, 1976 Google Scholar
  23. Schaffer, D.: Multiple objective optimization with vector evaluated genetic algorithm. In: Genetic Algorithm and their Applications: Proceedings of the First International Conference on Genetic Algorithm, pp. 93–100, 1985 Google Scholar
  24. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, London (1986) MATHGoogle Scholar
  25. Srivinas, N., Deb, K.: Multiobjective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994) CrossRefGoogle Scholar
  26. Talbi, E.-G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8, 541–564 (2002) CrossRefGoogle Scholar
  27. Ulungu, E.L., Teghem, J., Fortemps, P.: Heuristics for multi-objective combinatorial optimization by simulated annealing. In: Multiple Criteria Decision Making: Theory and Applications. Proceeding of the 6th National conference on Multiple Criteria Decision Making, pp. 228–238, Beijing, China, 1995 Google Scholar
  28. Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesmen: the genetic edge recombination operator. In: Proc. of the 3rd Int’l. Conf. on GAs. Morgan Kaufmann, San Mateo (1989) Google Scholar
  29. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999) Google Scholar
  30. Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: the strength Pareto approach. TIK-Report no. 43 (1998) Google Scholar
  31. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3, 257–271 (1999) CrossRefGoogle Scholar
  32. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Arnaud Zinflou
    • 1
  • Caroline Gagné
    • 1
  • Marc Gravel
    • 1
  • Wilson L. Price
    • 2
  1. 1.Département d’Informatique et de MathématiqueUniversité du Québec à ChicoutimiChicoutimiCanada
  2. 2.Faculté des Sciences de l’AdministrationUniversité LavalSte-FoyCanada

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