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The paper describes an interactive multiobjective memetic algorithm. During the run of the method the DM is periodically asked to compare a pair of generated solutions. The comparisons are used to focus the search in the promising region of the nondorninated set. The algorithm is evaluated on the multiobjective traveling salesperson problem with four, five and six objectives. It is also compared to an interactive evolutionary metaheuristic proposed by Phelps and Koksalan. The results of the computational experiment indicate that the interactive algorithm can efficiently find high quality solutions even in the case of multidimensional objective space.
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Rocznik
Tom
Strony
15--31
Opis fizyczny
Bibliogr. 18 poz.
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autor
- Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznań, Poland, jaszkiewicz@cs.put.poznan.pl
Bibliografia
- [1] Coello Coello C.A., Van Veldhuizen D.A., Lament G.B. (2002), Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers.
- [2] Cvetkovic D., Parmee I.C. (2002), Preferences and their application in evolutionary multiobjective optimization, IEEE Transactions on Evolutionary Computation, 6, 1, February 2002, 45-57.
- [3] Deb. K. (2001), Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons.
- [4] Doumpos M., Zapounidis C. (2004), A multicriteria classification approach based on pairwise comparison, European Journal of Operational Research, 158/2, 378-389.
- [5] Greco, S., Matarazzo, B., Słowiński, R. (2001), Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129/1, 1-47.
- [6] Ishibuchi H., Murata T. (1998), Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, 28, 3, 392-403.
- [7] Jaszkiewicz A. (2002). Genetic local search for multiobjective combinatorial optimization. European Journal of Operational Research, 137/1, 50-71.
- [8] Jaszkiewicz A. (2004), On the computational efficiency of multiobjective metaheuristies. The knapsack problem case study, European Journal of Operational Research, 158/2,418-433.
- [9] Jaszkiewicz A. (2003). Do Multiple-Objective Metaheuristics Deliver on Their Promises? A Computational Experiment on the Set-Covering Problem, IEEE Transactions on Evolutionary Computation, 7, 2, April 2003, 133-143.
- [10] Jaszkiewicz A. (2002), On the Computational Effectiveness of Multiobjective Melaheuristics, in: T. Trzaskalik, J. JVlichnik (eds.), Multiobjective and Goal Programming. Recent Developments, Physica-Verlag, Heidelberg, 86-100.
- [11] Jaszkiewicz A. (2004), A comparative study of multiple-objective metaheuristics on the bi-objective set covering problem and the Pareto memetic algorithm, Annals of Operations Research, T31 (1-4), October, 135-158.
- [12] Keeney R.L., Raiffa H. (1976), Decisions with Multiobjectives: Preferences and Value Tradeoffs, Wiley, New York.
- [13] Knowles J, Corne D. (2005), Memetic algorithms for multiobjective optimization: issues, methods and prospects, in: W.E. Hart, N. Krasnogor, J.E. Smith, Recent Advances in Memetic Algorithms, Springer, Studies in Fuzziness and Soft Computing, Vol. 166,313-352.
- [14] Merz P., Freisleben B. (1997), Genetic Local Search for the TSP: New Results, in Proceedings of the 1997 EEE International Conference on Evolutionary Computation, IEEE Press, 159-164.
- [15] Murata T., Ishibuchi H., Tanaka H. (1996), Multi-objective genetic algorithm and its application to flowshop scheduling, Computers Ind. Eng., 30, 4, 957-968.
- [16] Phelps S., Koksalan M. (2003), An interactive evolutionary metaheuristic for mulliobjective combinatorial optimization, Management Science, 49/12, 1726-1738, 2003.
- [17] Purshouse R.C. (2003), On the evolutionary optimisation of many objectives, Ph.D. thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield. [18]Steuer R.E. (1986), Multiple Criteria Optimization - Theory, Computation and Application, Wiley, New York.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BPP1-0073-0018