Abstract
This work introduces a multi-objective evolutionary algorithm capable of handling noisy problems like experimental setups with a particular emphasis on robustness against unexpected measurements (outliers). The algorithm is based on the Strength Pareto Evolutionary Algorithm (SPEA) of Zitzler and Thiele and includes the new concepts of domination dependent lifetime, re-evaluation of solutions and modifications in the update of the archive. Several tests on prototypical functions underline the improvements in convergence speed and robustness of the extended algorithm. The proposed algorithm is implemented to the Pareto optimization of the combustion process of a stationary gas turbine in an industrial setup. The free parameters of the optimization are the fuel injection rates through transverse jets. The Pareto front is constructed for the objectives of minimization of NO x emissions and reduction of the pressure fluctuations (pulsation) of the flame. Both objectives are conflicting affecting the environment and the lifetime of the turbine, respectively. The optimization leads a Pareto front corresponding to reduced emissions and pulsation of the burner. The physical implications of the solutions are discussed and the algorithm is evaluated.
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References
Horn, J.: Multicriterion decision making In Bäck, T., Fogel, B., D., Michalewicz, Z., Eds.: Handbook of Evolutionary Computation, Sec. F1.9: pp. 1–15. (1997)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. on Evolutionary Computation 3 (1999) 257–271
Van Veldhuizen, D.A., Lamont, G.B.: On measuring multiobjective evolutionary algorithm performance. In: Proceedings of the 2000 Congress on Evolutionary Computation. (2000) 204–211
Coello Coello, C.A.: An updated survey of evolutionary multiobjective optimization techniques: State of the art and future trends. In: Congress on Evolutionary Computation. (1999) 3–13
Coello Coello, C.A.: List of references on evolutionary multi-objective optimization, (http://www.lania.mx/~ccoello/EMOO/EMOObib.html, Last accessed July 2002)
Teich, J.: Pareto-front exploration with uncertain objectives. In et al., Z., ed.: Proceedings of the First Conference on Evolutionary Multi-Criterion Optimization. (2001)
Hughes, E.J.: Evolutionary multi-objective ranking with uncertainty and noise. In et al., Z., ed.: Proceedings of the First Conference on Evolutionary Multi-Criterion Optimization. (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company (1989)
Fonseca, M.C., Fleming, P.J.: Multi-objective genetic algorithms made easy: Selection, sharing and mating restrictions. In: Proceedings of the 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Application. (1995) 45–52
Pareto, V.: Manuale die Economia Politica. Societa Editrice Libraria, Milano, Italy (1906)
Goldberg, D.E., Segrest, P.: Finite Markov chain analysis of genetic algorithms. In Grafenstette, ed.: Proceedings of the Second International Conference on Genetic Algorithms. (1987)
Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W.: Proceedings of the First Conference on Evolutionary Multi-Criterion Optimization. Springer-Verlag (2001)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms. Analyzing state-of-the-art. Evolutionary Computation 8 (2000) 125–14
Miller, B.L., Goldberg, D.E.: Genetic algorithms, selection schemes, and the varying effects of noise. Illigal report no. 95005, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithm Laboratory (1995)
Bäck, T., Hoffmeister, F., Schwefel, H.P.: A survey of evolution strategies. In Belew, R.K., ed.: Proceedings of the Fourth International Conference on Genetic Algorithms and their Applications. (1991)
Deb, K.: Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation 7 (1999) 205–230
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© 2003 Springer-Verlag Wien
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Büche, D., Stoll, P., Koumoutsakos, P. (2003). Multi-Objective Evolutionary Algorithm for Optimization of Combustion Processes. In: Karagozian, A.R., Cortelezzi, L., Soldati, A. (eds) Manipulation and Control of Jets in Crossflow. International Centre for Mechanical Sciences, vol 439. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2792-6_12
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DOI: https://doi.org/10.1007/978-3-7091-2792-6_12
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00753-2
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