Abstract
Multiobjective optimization using evolutionary algorithms (EA) has become a wide area of research during these last years. However, only a few papers deal with the handling of preferences. To take them into account, a method based on multi-criteria decision aid, PROMETHEE II, has been implemented. Further-more, as the handling of the constraints is very critical in EA, an original approach (PAMUC Pr eferences Applied to MUltiobjectivity and Constraints) has been proposed, which considers the satisfaction of the constraints as another objective.
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References
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA (1989).
Z. Michalewicz, D. Dasgupta, R.G. Le Riche, M. Schoenauer, Evolutionary algorithms for constrained engineering problems, Computers & Industrial Engineering Journal, Vol.30, No.2, pp. 851–870 (1996).
C.A.C. Coello, Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art, Computer Methods in Applied Mechanics and Engineering, 191, pp. 1245–1287 (2002).
A.H.C. van Kampen, C.S. Strom, and L.M.C. Buydens, Lethalization, Penalty and Repair Functions for Constraint Handling in the Genetic Algorithm Methodology, Chemometrics and Intelligent Laboratory Systems, pp. 55–68 (1996).
D.A. Van Veldhuizen & G.B. Lamont, Multiobjective Evolutionary Algorithm Research: A History and Analysis, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB (1998).
C.A.C. Coello, A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques, Knowledge and Inform. Syst., An Intern. Journ., 1(3), pp. 269–308 (1999).
B. Rekiek, Assembly Line Design: Multiple Objective Grouping Evolutionary Algorithm and the Balancing of Mixed-Model Hybrid Assembly Line, PhD thesis, Université Libre de Bruxelles (2001).
P. De Lit, P. Latinne, B. Rekiek, A. Delchambre, Assembly planning with an ordering evolutionary algorithm, Intern. Journ. of Product. Research, vol. 39, n° 16, pp. 3623–3640 (2001).
J.-P. Brans, B. Mareschal, The PROMCALC and GAIA decision support system for multi-criteria decision aid, Decision Support Systems, North Holland, vol. 12, pp. 297–310 (1994).
J.A. Joines and C.R. Houck, On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs, Proceedings of the First IEEE International Conference on Evolutionary Computation, pp. 579–584, IEEE Press (1994).
Deb, An efficient constraint handling method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, 186, pp. 311–338 (2000).
C.A.C. Coello, Use of a self-adaptive penalty approach for engineering optimization problems, Comput. Ind. 41 (2), pp. 113–127 (2000).
D. Cvetkovi, Evolutionary Multi-Objective Decision Support Systems for Conceptual Design, PhD Thesis, School of Computing, Faculty of Technology, University of Plymouth (2000).
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Filomeno Coelho, R., Bouillard, P., Bersini, H. (2004). PAMUC: A New Method to Handle With Constraints and Multiobjectivity in Evolutionary Algorithms. In: Burczyński, T., Osyczka, A. (eds) IUTAM Symposium on Evolutionary Methods in Mechanics. Solid Mechanics and Its Applications, vol 117. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2267-0_9
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DOI: https://doi.org/10.1007/1-4020-2267-0_9
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