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A generalized varying-domain optimization method for fuzzy goal programming with priorities based on a genetic algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper proposes a generalized domain optimization method for fuzzy goal programming with different priorities. According to the three possible styles of the objective function, the domain optimization method and its generalization are correspondingly proposed. This method can generate the results consistent with the decision-maker's priority expectations, according to which the goal with higher priority may have higher level of satisfaction. However, the reformulated optimization problem may be nonconvex for the reason of the nature of the original problem and the introduction of the varying-domain optimization method. It is possible to obtain a local optimal solution for nonconvex programming by the SQP algorithm. In order to get the global solution of the new programming problem, the co-evolutionary genetic algorithm, called GENOCOP III, is used instead of the SQP method. In this way the decision-maker can get. the optimum of the optimization problem. We demonstrate the power of this proposed method based on genetic algorithm by illustrative examples.
Rocznik
Strony
633--652
Opis fizyczny
Bibliogr. 37 poz., wykr.
Twórcy
autor
  • Institute of Automation Shanghai Jiao Tong University Shanghai, 200030, P.R. China
autor
  • Institute of Automation Shanghai Jiao Tong University Shanghai, 200030, P.R. China
autor
  • Institute of Automation Shanghai Jiao Tong University Shanghai, 200030, P.R. China
Bibliografia
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  • Chen, H.K. (1994) A note on a fuzzy goal programming algorithms by Tiwari Dharmar and Rao. Fuzzy Sets and Systems 62, 287-290.
  • Chen, L.H. and Tsai, F.C. (2001) Fuzzy goal programming with different importance and priorities. European Journal of Operational Research 133, 548-556.
  • Chou, H.H., Premkumar, G. and Chu, C.H. (2001) Genetic algorithms for communications network design - an empirical study of the factors that influence performance. IEEE Transactions on evolutionary comutation 5 (3), 236-249.
  • Flavell, R.B. (1976) A new goal programming formulation. Omega 4, 731-732.
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  • Hamalainen, R.P. and Mantysaari, J. (2002) Dynamic multi-objective heating optimization. European Journal of Operational Research 142, 1-15.
  • Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Ann Arbor, MI.
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  • Jensen, M.T. (2003) Generating robust and flexible job shop schedules using genetic algorithms. IEEE Transactions on Evolutionary Computation 7 (5), 275-288.
  • Kato, K. and Sakawa, M. (1998) An interactive fuzzy satisficing method for large scale multi-objective 0-1 programming problems with fuzzy parameters through genetic algorithms. European Journal of Operational Research 107, 590-598.
  • Michalewicz, Z. (1996) Genetic Algorithms + Data Structure = Evolution Programs, third, revised and extended edition. Springer-Verlag, NewYork.
  • Michalewicz, Z. and Nazhiyath, G. (1995) Genocop III: A co-evolutionary algorithms for Numerical Optimization Problems with Nonlinear Constraints. IEEE Proc. Second Int. Conf. Evolutionary Comput., Perth, Australia, 647-651.
  • Michalewicz, Z. and Schoenauer, M. (1996) Evolutionary Algorithm for Constrained Parameter Optimization problems. Evolutionary Comput. 4 (1), 1-32.
  • Mohamed, R.H. (1997) The relationship between goal programming and fuzzy programming. Fuzzy Sets and Systems 89, 215-222.
  • Narasimhan, R. (1980) Goal programming in a fuzzy environment. Decisionsand Sciences11, 325-336.
  • Pongcharoen, P., Hicks, C. and Braiden, P.M. (2004) The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure. European Journal of Operational Research 152 (1), 215-225.
  • Rao, S.S., Sundararaju, K., Prakash, B.G. and Balakrishna, C.(1992) Fuzzy goal programming approach for structural optimization. AIAA Journal 30 (5), 1425-1432.
  • Rao, J.R., Tiwari, R.N. and Mohanty, B.K. (1988) A preference structure on aspiration levels in a goal programming problem-a fuzzy approach. Fuzzy Sets and Systems 25, 175-182
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  • Sakawa, M. and Yauchi, K. (2001) An interactive fuzzy satisficing methodfor multi-objective non-convex programming problems withfuzzy members through co-evolutionary genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics-Part A 31 (3), 459-467.
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  • Tiwari, R.N., Dharmar, S. and Rao, J.R. (1986) Priority structure in fuzzy goal programming. Fuzzy Sets and Systems 19, 251-259.
  • Tiwari, R.N., Mohanty, B.K. and Rao, J.R. (1987) Fuzzy goal programming - an additive model. Fuzzy Sets and Systems 24, 27-34.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0007-0074
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