An Interactive Evolutionary Metaheuristic for Multiobjective Combinatorial Optimization

References

  • Alves M. J., Climaco J. An interactive method for 0-1 multiobjective problems using simulated annealing and tabu search. J. Heuristics (2000) 6:385–403CrossrefGoogle Scholar
  • Beasley J. E., Chu P. C. A genetic algorithm for the multidimensional knapsack problem. J. Heuristics (1998) 4:63–86CrossrefGoogle Scholar
  • Borges C. C. H., Barbarosa H. J. C. A non-generational genetic algorithm for multiobjective optimization. 2000 Congress Evolutionary Computation (2000) 172–179IEEE, San Diego, CAGoogle Scholar
  • Coello C. A. Handling preferences in evolutionary multiobjective optimization: A survey. 2000 Congress Evolutionary Computation Proc. Inst. Electr. Electronics Engineers (2000) (IEEE, Piscataway, NJ) Google Scholar
  • Czyzak P., Jaszkiewicz A. Pareto simulated annealing—A metaheuristic technique for multiple-objective combinatorial optimization. J. Multicriteria Decision Anal. (1998) 7:34–47CrossrefGoogle Scholar
  • Ehrgott M., Gandibleux X. An annotated bibliography of multiobjective combinatorial optimization. OR Spektrum (2000) 22:425–460CrossrefGoogle Scholar
  • Fonseca C. M., Fleming P. J., Forest S. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. Proc. Fifth Internat. Conf. Genetic Algorithms (1993) (Morgan Kauffman, San Mateo, CA) 416–423Google Scholar
  • Gandibleux X., Freville A. Tabu search based procedure for solving the 0-1 multiobjective knapsack problem: The two objectives case. J. Heuristics (2000) 6:361–383CrossrefGoogle Scholar
  • Hamacher H. W., Ruhe G. On spanning tree problems with multiple objectives. Ann. Oper. Res. (1994) 52:209–230CrossrefGoogle Scholar
  • Hapke M., Jaszkiewicz A., Slowinski R. Interactive analysis of multiple-criteria project scheduling problems. Eur. J. Oper. Res. (1998) 107:315–324CrossrefGoogle Scholar
  • Horn J., Nafpliotis N., Goldberg D. E. A niched Pareto genetic algorithm for multiobjective optimization. Proc. First Inst. Electr. Electronics Engineers Conf. Evolutionary Computation (1994) (NJ)82–87PiscatwayGoogle Scholar
  • Klamroth K., Wiecek M. M. Dynamic programming approaches to the multiple criteria knapsack problem. Naval Res. Logist. (2000) 47:57–76CrossrefGoogle Scholar
  • Köksalan M. M., Sagala P. N. S. Interactive approaches for discrete alternative multiple criteria decision making with monotone utility functions. Management Sci. (1995) 41:1158–1171LinkGoogle Scholar
  • Köksalan M., Karwan M. H., Zionts S. An improved method for solving multiple criteria problems involving discrete alternatives. IEEE Trans. Systems, Man, Cybernetics (1984) SMC-14:24–34CrossrefGoogle Scholar
  • Martello S., Toth P.Knapsack Problems: Algorithms and Computer Implementations (1990) (John Wiley and Sons, Chichester, England) Google Scholar
  • Michaelwicz Z., Arabas J., Ras Z. W., Zamankova M. Genetic algorithms for the 0/1 knapsack problem. Methodologies for Intelligent Systems (ISMIS '94) (1994) 134–143Springer, Charlotte, NCCrossrefGoogle Scholar
  • Reeves C. R., Reeves C. R. Genetic algorithms. Modern Heuristic Techniques for Combinatorial Problems (1993) (Halsted Press, New York) Google Scholar
  • Steuer R. E.Multiple Criteria Optimization (1986) (John Wiley and Sons, New York) Google Scholar
  • Ulungu E. L., Teghem J. Multiobjective combinatorial optimization problems: A survey. J. Multicriteria Decision Anal. (1994) 3:83–104CrossrefGoogle Scholar
  • Ulungu E. L., Teghem J., Climaco J. Solving multiobjective knapsack problem by a branch-and-bound procedure. Multicriteria Analysis (1997) (Springer-Verlag, Berlin, Germany) 269–278Google Scholar
  • Ulungu E. L., Teghem J., Ost C. Efficiency of interactive multi-objective simulated annealing through a case study. J. Oper. Res. Soc. (1998) 49:1044–1050Google Scholar
  • Ulungu E. L., Teghem J., Fortemps P. H., Tuyttens D. MOSA method: A tool for solving multiobjective combinatorial optimization problems. J. Multicriteria Decision Anal. (1999) 8:221–236CrossrefGoogle Scholar
  • Zhou G., Gen M. Genetic algorithm on multi-criteria minimum spanning tree problem. Eur. J. Oper. Res. (1999) 114:141–152CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.