A preference based interactive evolutionary algorithm for multi-objective optimization: PIE

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6576 LNCS, Issue , 2011, Pages 212-225

  Sindhya, Karthik ^a   Ruiz, Ana Belen ^b   Miettinen, Kaisa ^a  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^b UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKERS; INTERACTIVE EVOLUTIONARY ALGORITHMS; MULTI OBJECTIVE; MULTIPLE CRITERIA DECISION MAKING; OBJECTIVE FUNCTION VALUES; PARETO FRONT; PARETO OPTIMAL SOLUTIONS; POLLUTION MONITORING; PREFERENCE-BASED; SCALARIZING FUNCTION;

DECISION MAKING; MULTIOBJECTIVE OPTIMIZATION; AIR NAVIGATION; ITERATIVE METHODS; OPTIMIZATION; PARETO PRINCIPLE;

EVOLUTIONARY ALGORITHMS;

EID: 79953824194     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19893-9_15     Document Type: Conference Paper

References (24)

1. Branke, J., Kaubler, T., Schmeck, H.: Guidence in evolutionary multi-objective optimization. Advances in Engineering Software 32(6), 499-507 (2001)
2. Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. Elsevier Science Publishing Co., Inc., New York (1983)
3. Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)
4. Deb, K.: Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)
5. Deb, K., Kumar, A.: Light beam search based multi-objective optimization using evolutionary algorithms. In: Proceedings of the Congress on Evolutionary Computation (CEC 2007), pp. 2125-2132. IEEE Press, Los Alamitos (2007)
6. Deb, K., Miettinen, K., Chaudhuri, S.: Towards an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Transactions on Evolutionary Computation 14(6), 821-841 (2010)
7. Deb, K., Saxena, D.K.: Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation (CEC 2006), pp. 3353-3360. IEEE Press, Los Alamitos (2006)
8. Deb, K., Sinha, A., Korhonen, P.J., Wallenius, J.: An interactive evolutionary multi-objective optimization method based on progressively approximated value functions. Tech. Rep. 2009005, KanGAL (2009)
9. Deb, K., Sundar, J., Rao, U.B., Chaudhuri, S.: Reference point based multiobjective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research 2(3), 273-286 (2006)
10. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 825-830. IEEE Press, Los Alamitos (2002)
11. Hakanen, J., Miettinen, K., Mäkelä, M.M.: Using genetic algorithms in multiobjective process optimization. In: Bugeda, G., et al. (eds.) Proceedings of the Congress on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (EUROGEN 2003), CD-Proceedings, CIMNE, Barcelona (2003)
12. Imai, A., Sasaki, K., Nishimura, E., Papadimitriou, S.: Multi-objective simultaneous stowage and load planning for a container ship with container rehandle in yard stacks. European Journal of Operational Research 171(2), 373-389 (2006)
13. Knowles, J., Corne, D.: Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. In: Obayashi, S., et al. (eds.) EMO 2007. LNCS, vol. 4403, pp. 757-771. Springer, Heidelberg (2007)
14. Luque, M., Miettinen, K., Eskelinen, P., Ruiz, F.: Incorporating preference information in interactive reference point methods for multiobjective optimization. Omega 37(2), 450-462 (2009)
15. Luque, M., Ruiz, F., Miettinen, K.: Global formulation for interactive multiobjective optimization. OR Spectrum 33(1), 27-48 (2011)
16. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)
17. Miettinen, K.: Using interactive multiobjective optimization in continuous casting of steel. Materials and Manufacturing Processes 22, 585-593 (2007)
18. Miettinen, K., Eskelinen, P., Ruiz, F., Luque, M.: NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point. European Journal of Operational Research 206(2), 426-434 (2010)
19. Miettinen, K., Lotov, A.V., Kamenev, G.K., Berezkin, V.E.: Integration of two multiobjective optimization methods for nonlinear problems. Optimization Methods and Software 18(1), 63-80 (2003)
20. Ruiz, F., Luque, M., Cabello, J.M.: A classification of the weighting schemes in reference point procedures for multiobjective programming. Journal of the Operations Research Society 60, 544-553 (2009)
21. Thiele, L., Miettinen, K., Korhonen, P.J., Molina, J.: A preference-based evolutionary algorithm for multi-objective optimization. Evolutionary Computation 17(3), 411-436 (2009)
22. Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211, 453-458 (1981)
23. Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making: Theory and Application, pp. 468-486. Springer, Hagen (1980)
24. Wierzbicki, A.P.: On the completeness and constructiveness of parametric characterizations to vector optimization problems. OR Spektrum 8, 73-87 (1986)