An Evolutionary Metaheuristic for Approximating Preference-Nondominated Solutions

References

• Athanassopoulos A. D., Podinovski V. V. Dominance and potential optimality in multiple criteria decision analysis with imprecise information. J. Oper. Res. Soc. (1997) 48:142–150Crossref, Google Scholar
• Coello Coello C. A. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Inform. Systems (1999) 1:269–308Crossref, Google Scholar
• Coello Coello C. A. Handling preferences in evolutionary multiobjective optimization: A survey. 2000 Congress Evolutionary Comput. (2000) 1:30–37Crossref, Google Scholar
• Czyzak P., Jaszkiewicz A. Pareto simulated annealing—A metaheuristic technique for multiple-objective combinatorial optimization. J. Multi-Criteria Decision Anal. (1998) 7:34–47Crossref, Google Scholar
• Deb K.Multi-Objective Optimization Using Evolutionary Algorithms (2001) (Wiley, Chichester, UK) Google Scholar
• Ehrgott M., Gandibleux X., Ehrgott M., Gandibleux X. Multiobjective combinatorial optimization. Multiple Criteria Optimization—State of the Art Annotated Bibliographic Surveys (2002) 52(Kluwer Academic Publishers, Boston, MA) 369–444Crossref, Google Scholar
• Ehrgott M., Gandibleux X. Approximative solution methods for multiobjective combinatorial optimization. TOP (2004) 12:1–89Crossref, Google Scholar
• Fonseca C. M., Fleming P. J. Multiobjective optimization and multiple constraint handling with evolutionary algorithms—Part I: A unified formulation. IEEE Trans. SMC-Part A: Systems and Humans (1998) 28:26–37Crossref, Google Scholar
• Gandibleux X., Mezdaoui N., Freville A., Caballero R., Steuer R. A tabu search procedure to solve multiobjective combinatorial optimization problems. Proc. MOPGP’96 (1996) (Springer-Verlag, Berlin) Google Scholar
• Hajela P., Lin C. Y. Genetic search strategies in multicriteria optimal design. Structural Optim. (1992) 4:99–107Crossref, Google Scholar
• Hazen G. B. Partial information, dominance, and potential optimality in multiattribute utility theory. Oper. Res. (1986) 34:296–310Link, Google Scholar
• Köksalan M. M., Sagala P. N. S. Interactive approaches for discrete alternative multiple criteria decision making with monotone utility functions. Management Sci. (1995) 41:1158–1171Link, Google Scholar
• Phelps S., Köksalan M. An interactive evolutionary metaheuristic for multiobjective combinatorial optimization. Management Sci. (2003) 49:1726–1738Link, Google Scholar
• Reeves C. R.Modern Heuristic Techniques for Combinatorial Problems (1993) (Wiley, New York) Google Scholar
• Steuer R. E.Multiple Criteria Optimization: Theory, Computation, and Application (1986) (Wiley, New York) Google Scholar
• Stewart T. J. A critical survey on the status of multiple criteria decision making theory and practice. OMEGA Internat. J. Management Sci. (1992) 20:569–586Crossref, Google Scholar
• Teghem J., Tuyttens D., Ulungu E. L. An interactive heuristic method for multiobjective combinatorial optimization. Comput. Oper. Res. (2000) 27:621–634Crossref, Google Scholar
• Ulungu E. L., Teghem J. Multi-objective combinatorial optimization problems: A survey. J. Multi-Criteria Decision Anal. (1994) 3:83–104Crossref, Google Scholar
• Weber M. Decision making with incomplete information. Eur. J. Oper. Res. (1987) 28:44–57Crossref, Google Scholar
• Zitzler E., Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evolutionary Comput. (1999) 3:257–271Crossref, Google Scholar