Abstract
The design of a new product or of its manufacturing process consists in reconciling multiple objectives with each other to take into account their different features. In this paper, a new multicriteria optimization algorithm is presented. This method is based on the use of (i) a genetic algorithm (GA) which optimizes each system response and (ii) a selection algorithm which sorts Pareto-efficient points. This technique presents the great advantage of being of wide use. There is no particular mathematical condition about functions that are simultaneously optimized and, unlike the other multicriteria optimization methods which depend on the user's choice, our algorithm permits to obtain an optimal surface in which the user will be able to pick up his own working conditions. Efficiency of this new method is here illustrated with one mathematical example and with an industrial application.
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References
Bicking F., Fonteix C., Corriou J.P., Marc I.: Global Optimization by Artificial Life: a new technique using Genetic Population Evolution. RAIRO-Operation Research, 1, 28 (1994) 23–36
Courcoux P., Qannari E.M., Melcion J.P., Morat J.L.: Optimisation multiréponse: application à un procédé de granulation d'aliments. Récents Progrès en Génie des Procédés: Statégie expérimentale et procédés biotechnologiques, 36, 9 (1995) 41–47
Derringer G., Suich R.: Simultaneous optimization of several response variables. Journal of quality Technology 12 (1980) 214–219
Fonteix C., Bicking F., Perrin E., Marc I.: Haploid and diploid algorithms, a new-approach for global optimization: compared performances. Int. J. of Systems Science (to appear)
Horn J., Nafpliotis N., Goldberg D. E.: A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the first Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, 1 (1994) 82–87
Korhonen P., Laakso J.: A visual interactive method for solving the multiple criteria problem. European Journal of Operational Research 24 (1986) 277–287
Kouada I.: Sur la propriété de domination et l'existence de points Pareto-efficaces en optimisation vectorielle convexe. RAIRO-Operation Research 1, 28 (1994) 77–84
Logothetis N., Haigh A.: Characterizing and optimizing Multi-response Processes by the Taguchi Method. Quality and Realibility Engineering International 4 (1988) 159–169
Narula S.C., Kirilov L., Vassilev V.: Reference Direction Approach for solving Multiple Objective Nonlinear Programming Problems. IEEE Transactions on Systems Man and Cybernetics 5, 24 (1994) 804–806
Steuer R.E.: Multiple criteria optimization: theory, computation and application. J. Wiley & Sons ed. (1986)
Tarvainen K.: Generating Pareto optimal alternatives by non feasible hierarchical method. Journal of Optimization Theory and Applications 1, 80 (1994) 181–185
Viennet R., Fonteix c., Marc I.: Multicriteria optimization using a genetic algorirhm for determining a set of Pareto. accepted for publication in Journal of Systems Sciences.
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© 1996 Springer-Verlag Berlin Heidelberg
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Viennet, R., Fonteix, C., Marc, I. (1996). New multicriteria optimization method based on the use of a diploid genetic algorithm: Example of an industrial problem. In: Alliot, JM., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds) Artificial Evolution. AE 1995. Lecture Notes in Computer Science, vol 1063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61108-8_34
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DOI: https://doi.org/10.1007/3-540-61108-8_34
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