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23. MCDA and Multiobjective Evolutionary Algorithms

verfasst von : Juergen Branke

Erschienen in: Multiple Criteria Decision Analysis

Verlag: Springer New York

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Abstract

Evolutionary multiobjective optimization promises to efficiently generate a representative set of Pareto optimal solutions in a single optimization run. This allows the decision maker to select the most preferred solution from the generated set, rather than having to specify preferences a priori. In recent years, there has been a growing interest in combining the ideas of evolutionary multiobjective optimization and MCDA. MCDA can be used before optimization, to specify partial user preferences, after optimization, to help select the most preferred solution from the set generated by the evolutionary algorithm, or be tightly integrated with the evolutionary algorithm to guide the optimization towards the most preferred solution. This chapter surveys the state of the art of using preference information within evolutionary multiobjective optimization.

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Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Articulating user preference in many-objective problems by sampling the weighted hypervolume. In: Raidl, G., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 555–562. ACM, New York (2009)

Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Investigating and exploiting the bias of weighter hypervolume to articulate user preferences. In: Raidl, G., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 563–570. ACM, New York (2009)

Barbosa, H.J.C., Barreto, A.M.S.: An interactive genetic algorithm with co-evolution of weights for multiobjective problems. In: Spector, L., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 203–210. Morgan Kaufmann, Los Altos, CA (2001)

Battiti, R., Passerini, A.: Brain-computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker. IEEE Trans. Evol. Comput. 14(5), 671–687 (2010)CrossRef

Branke, J.: Consideration of user preferences in evolutionary multi-objective optimization. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (eds.) Multiobjective Optimization—Interactive and Evolutionary Approaches. Lecture Notes in Computer Science, vol. 5252, pp. 157–178. Springer, Berlin (2008)

Branke, J., Deb, K.: Integrating user preference into evolutionary multi-objective optimization. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 461–478. Springer, Berlin (2005)CrossRef

Branke, J., Kaußler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Adv. Eng. Softw. 32, 499–507 (2001)CrossRef

Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding knees in multi-objective optimization. In: Parallel Problem Solving from Nature. Lecture Notes in Computer Science, vol. 3242, pp. 722–731. Springer, Berlin (2004)

Branke, J., Greco, S., Słowiński, R., Zielniewicz, P.: Interactive evolutionary multiobjective optimization using robust ordinal regression. In: Ehrgott, M., et al. (eds.) International Conference on Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, vol. 5467, pp. 554–568. Springer, Berlin (2009)CrossRef

10.

Branke, J., Greco, S., Słowiński, R., Zielniewicz, P.: Interactive evolutionary multiobjective optimization driven by robust ordinal regression. Bull. Pol. Acad. Sci. Tech. Sci. 58(3), 347–358 (2010)

11.

Brans, J.P., Mareschal, B.: PROMETHEE methods. In: Figueira, J., et al. (eds.) Multiple Criteria Decision Analysis, pp. 163–196. Springer, Berlin (2005)

12.

Braun, M.A., Shukla, P.K., Schmeck, H.: Preference ranking schemes in multi-objective evolutionary algorithms. In: Takahashi, R.H.C., et al. (eds.) Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, vol. 6576, pp. 226–240. Springer, Berlin (2011)CrossRef

13.

Bringmann, K., Friedrich, T.: An efficient algorithm for computing hypervolume contributions. Evol. Comput. J. 18(3), 383–402 (2010)CrossRef

14.

Campigotto, P., Passerini, A.: Adapting to a realistic decision maker: experiments towards a reactive multi-objective optimizer. In: International Conference on Learning and Intelligent Optimization. Lecture Notes in Computer Science, vol. 6073, pp. 338–341. Springer, Berlin (2010)

15.

Coello, C.A.C.: Handling preferences in evolutionary multiobjective optimization: a survey. In: Congress on Evolutionary Computation, vol. 1, pp. 30–37. IEEE (2000)

16.

Coello, C.A.C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11–12), 1245–1287 (2002)CrossRef

17.

Coello, C.A.C., Veldhuizen, D.A.V., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, Dordrecht (2002)CrossRef

18.

Coelho, R.F., Bersini, H., Bouillard, P.: Parametrical mechanical design with constraints and preferences: application to a purge valve. Comput. Methods Appl. Mech. Eng. 192, 4355–4378 (2003)CrossRef

19.

Cvetkovic, D., Parmee, I.C.: Preferences and their application in evolutionary multiobjective optimisation. IEEE Trans. Evol. Comput. 6(1), 42–57 (2002)CrossRef

20.

Das, I.: On characterizing the ‘knee’ of the pareto curve based on normal-boundary intersection. Struct. Optim. 18(2/3), 107–115 (1999)CrossRef

21.

Deb, K.: Solving goal programming problems using multi-objective genetic algorithms. In: Proceedings of Congress on Evolutionary Computation, pp. 77–84 (1999)

22.

Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)CrossRef

23.

Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, New York (2001)

24.

Deb, K.: Multi-objective evolutionary algorithms: introducing bias among Pareto-optimal solutions. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: Theory and Applications, pp. 263–292. Springer, London (2003)CrossRef

25.

Deb, K., Chaudhuri, S.: I-MODE: an interactive multi-objective optimization and decision-making using evolutionary methods. Technical Report KanGAL Report No. 2007003, Indian Institute of Technology Kanpur (2007)

26.

Deb, K., Kumar, A.: Interactive evolutionary multi-objective optimization and decision-making using reference direction method. In: Genetic and Evolutionary Computation Conference, pp. 781–788. ACM, New York (2007)

27.

Deb, K., Kumar, A.: Light beam search based multi-objective optimization using evolutionary algorithms. In: Congress on Evolutionary Computation, pp. 2125–2132. IEEE, Los Alamitos (2007)

28.

Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Genetic and Evolutionary Computation Conference, pp. 635–642. ACM, New York (2006)

29.

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

30.

Deb, K., Sundar, J., Udaya Bhaskara Rao, N., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comput. Intell. Res. 2(3), 273–286 (2006)

31.

Deb, K., Sinha, A., Korhonen, P., Wallenius, J.: An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. IEEE Trans. Evol. Comput. 14(5), 723–739 (2010)CrossRef

32.

Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)CrossRef

33.

Emmerich, M., Beume, N., Naujoks, B.: An EMO algorithm using the hypervolume measure as selection criterion. In: Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, vol. 3410. Springer, Berlin (2005)

34.

Fernandez, E., Lopez, E., Bernal, S., Coello, C.A.C., Navarro, J.: Evolutionary multiobjective optimization using an outranking-based dominance generalization. Comput. Oper. Res. 37(2), 390–395 (2010)CrossRef

35.

Fernandez, E., Lopez, E., Lopez, F., Coello, C.A.C.: Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: the extended NOSGA method. Inf. Sci. 181, 44–56 (2011)CrossRef

36.

Figueira, J., Mousseau, V., Roy, B.: ELECTRE methods. In: Figueia, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 134–162. Springer, Berlin (2005)CrossRef

37.

Figueira, J., Greco, S., Słowiński, R.: Building a set of additive value functions representing a reference preorder and intensities of preference: Grip method. Eur. J. Oper. Res. 195(2), 460–486 (2009)CrossRef

38.

Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion, and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)

39.

Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms—part I: a unified formulation. IEEE Trans. Syst. Man Cybern. Part A 28(1), 26–37 (1998)CrossRef

40.

Fowler, J.W., Gel, E.S., Köksalan, M.M., Korhonen, P., Marquis, J.L., Wallenius, J.: Interactive evolutionary multi-objective optimization for quasi-concave preference functions. Eur. J. Oper. Res. 206, 417–425 (2010)CrossRef

41.

Friedrich, T., Kroeger, T., Neumann, F.: Weighted preferences in evolutionary multi-objective optimization. Int. J. Mach. Learn. Cybern. (to appear) 4(2), 139–148, Springer (2013)

42.

Gong, M., Liu, F., Zhang, W., Jiao, L., Zhang, Q.: Interactive MOEA/D for multi-objective decision making. In: Genetic and Evolutionary Computation Conference, pp. 721–728. ACM, New York (2011)

43.

Greco, S., Słowiński, R., Figueira, J., Mousseau, V.: Robust ordinal regression. In: Ehrgott, M., et al. (eds.) New Advances in Multiple Criteria Decision Analysis, pp. 273–320. Springer, Berlin (2010)

44.

Greenwood, G.W., Hu, X.S., D’Ambrosio, J.G.: Fitness functions for multiple objective optimization problems: combining preferences with Pareto rankings. In: Belew, R.K., Vose, M.D. (eds.) Foundations of Genetic Algorithms, pp. 437–455. Morgan Kaufmann, Los Altos, CA (1997)

45.

Hapke, M., Jaszkiewicz, A., Słowiński, R.: Interactive analysis of multiple-criteria project scheduling problems. Eur. J. Oper. Res. 107, 315–324 (1998)CrossRef

46.

Hughes, E.J.: Constraint handling with uncertain and noisy multi-objective evolution. In: Congress on Evolutionary Computation, pp. 963–970. IEEE, Los Alamitos (2001)

47.

Jaimes, A.L., Montano, A.A., Coello, C.A.C.: Preference incorporation to solve many-ojective airfoil design problems. In: Congress on Evolutionary Computation, pp. 1605–1612. IEEE, New Orleans (2011)

48.

Jaszkiewicz, A., Slowinski, R.: The light beam search over a non-dominated surface of a multiple-objective programming problem. Eur. J. Oper. Res. 113(2), 300–314 (1999)CrossRef

49.

Jaszkiewicz, A.: A comparative study of multiple-objective metaheuristics on the bi-objective set covering problem and the pareto memetic algorithm. Ann. Oper. Res. 131(1–4), 135–158 (2004)CrossRef

50.

Jaszkiewicz, A.: Interactive multiobjective optimization with the Pareto memetic algorithm. Found. Comput. Decis. Sci. 32(1), 15–32 (2007)

51.

Jiménez, F., Verdegay, J.L.: Evolutionary techniques for constrained optimization problems. In: Zimmermann, H.J. (ed.) European Congress on Intelligent Techniques and Soft Computing. Verlag Mainz, Germany (1999)

52.

Jin, Y., Sendhoff, B.: Incorporation of fuzzy preferences into evolutionary multiobjective optimization. In: Wang, L., et al. (eds.) Asia-Pacific Conference on Simulated Evolution and Learning, pp. 26–30. Nanyang Technical University, Singapore (2002)

53.

Kamalian, R., Takagi, H., Agogino, A.M.: Optimized design of mems by evolutionary multi-objective optimization with interactive evolutionary computation. In: Genetic and Evolutionary Computation Conference, pp. 1030–1041 (2004)

54.

Karahan, I., Köksalan, M.: A territory defining multiobjective evolutionary algorithmsw and preference incorporation. IEEE Trans. Evol. Comput. 14(4), 636–664 (2010)CrossRef

55.

Köksalan, M., Karahan, I.: An interactive territory defining evolutionary algorithm: iTDEA. IEEE Trans. Evol. Comput. 14(5), 702–722 (2010)CrossRef

56.

Korhonen, P., Laakso, J.: A visual interactive method for solving the multiple criteria problem. Eur. J. Oper. Res. 24, 277–287 (1986)CrossRef

57.

Korhonen, P., Wallenius, J., Zionts, S.: Solving the discrete multiple criteria problem using convex cones. Manage. Sci. 30, 1336–1345 (1984)CrossRef

58.

Luque, J.M., Santana-Quintero, L.V., Hernandez-Diaz, A.G., Coello, C.A.C., Caballero, R.: g-dominance: reference point based dominance for multiobjective metaheuristics. Eur. J. Oper. Res. 197(2), 685–692 (2009)

59.

Parreiras, R.O., Vasconcelos, J.A.: Decision making in multiobjective optimization problems. In: Nedjah, N., de Macedo Mourelle, L. (eds.) Real-World Multi-Objective System Engineering, pp. 29–52. Nova Science Publishers, New York (2005)

60.

Phelps, S., Köksalan, M.: An interactive evolutionary metaheuristic for multiobjective combinatorial optimization. Manage. Sci. 49(12), 1726–1738 (2003)CrossRef

61.

Rachmawati, L., Srinivasan, D.: Preference incorporation in multi-objective evolutionary algorithms: a survey. In: Congress on Evolutionary Computation, pp. 3385–3391. IEEE, Los Alamitos (2006)

62.

Rekiek, B., Lit, P.D., Fabrice, P., L’Eglise, T., Emanuel, F., Delchambre, A.: Dealing with users’s preferences in hybrid assembly lines design. In: Binder, Z., et al. (eds.) Management and Control of Production and Logistics Conference, pp. 989–994. Pergamon, New York (2000)

63.

Said, L.B., Bechikh, S., Ghedira, K.: The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans. Evol. Comput. 14(5), 801–818 (2010)CrossRef

64.

Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of ec optimization and human evaluation. In: Proceedings of the IEEE, vol. 89, pp. 1275–1296 (2001)

65.

Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms with goal and priority information for multi-objective optimization. In: Congress on Evolutionary Computation, pp. 106–113. IEEE, Los Alamitos (1999)

66.

Thiele, L., Miettinen, K., Korhonen, P.J., Molina, J.: A preference-based interactive evolutionary algorithm for multiobjective optimization. Evol. Comput. J. 17(3), 411–436 (2009)CrossRef

67.

Todd, D.S., Sen, P.: Directed multiple objective search of design spaces using genetic algorithms and neural networks. In: Banzhaf, W., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 1738–1743. Morgan Kaufmann, San Francisco, CA (1999)

68.

Trautmann, H., Mehnen, J.: A method for including a-priori-preference in multicriteria optimization. Technical Report 49/2005, SFG 475, University of Dortmund, Germany (2005)

69.

Wagner, T., Trautmann, H.: Integration of preferences in hypervolume-based multiobjective evolutionary algorithms by means of desirability functions. IEEE Trans. Evol. Comput. 14(5), 688–701 (2010)CrossRef

70.

White, C., Sage, A., Dozono, S.: A model of multiattribute decision-making and tradeoff weight determination under uncertainty. IEEE Trans. Syst. Man Cybern. 14, 223–229 (1984)CrossRef

71.

Wierzbicki, A.P.: Basic properties of scalarizing functions for multiobjective optimization. Optimization 8(1), 55–60 (1977)

72.

Wierzbicki, A.P.: On the completeness and constructiveness of parametric characterizations to vector optimization problems. OR Spektrum 8(2), 73–87 (1986)CrossRef

73.

Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. Evol. Comput. 11(6), 712–731 (2007)CrossRef

74.

Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Parallel Problem Solving from Nature. Lecture Notes in Computer Science, vol. 3242, pp. 832–842. Springer, Berlin (2004)

75.

Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100. International Center for Numerical Methods in Engineering, Spain (2002)

76.

Zitzler, E., Brockhoff, D., Thiele, L.: The hypervolume indicator revisited: on the design of Pareto-compliant indicators via weighted integration. In: Obayashi, S., et al. (eds.) Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, vol. 4403, pp. 862–876. Springer, Berlin (2007)CrossRef

Titel: MCDA and Multiobjective Evolutionary Algorithms
verfasst von: Juergen Branke
Verlag: Springer New York
Buch: Multiple Criteria Decision Analysis
Print ISBN: 978-1-4939-3093-7

Electronic ISBN: 978-1-4939-3094-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-1-4939-3094-4_23