Abstract
Adaptive Cruise Control (ACC) systems represent an active research area in the automobile industry. The design of such systems typically involves several, possibly conflicting criteria such as driving safety, comfort and fuel consumption. When the different design objectives cannot be met simultaneously, a number of non-dominated solutions exists, where no single solution is better than another in every aspect. The knowledge of this set is important for any design decision as it contains valuable information about the design problem at hand.
In this paper we approximate the non-dominated set of a given ACC-controller design problem for trucks using multi-objective evolutionary algorithms (MOEAs). Two different search strategies based on a continuous relaxation and on a direct representation of the integer design variables are applied and compared to a grid search method.
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References
K. Deb. Multi-objective optimization using evolutionary algorithms. Wiley, Chichester, UK, 2001.
M. Emmerich, M. Grötzner, B. Groß, and M. Schütz. Mixed-integer evolution strategy for chemical plant optimization with simulators. In I. C. Parmee, editor, Evolutionary Design and Manufacutre — Selected papers from ACDM’00, pages55–67, Plymouth, UK, April 26–28, 2000. Springer, London.
J. Ludmann. Beeinflussung des Verkehrsablaufs auf Strassen: Analyse mit dem fahrzeugorientierten Verkehrssimulationsprogramm PELOPS. Schriftenreihe Automobiltechnik. Forschungsgesellschaft Kraftfahrwesen mbH Aachen, 1998. (in German).
G. Rudolph. An evolutionary algorithm for integer programming. In Y. Davidor, H.-P. Schwefel, and R. Männer, editors, Parallel Problem Solving from Nature-PPSN III, Int’l Conf. Evolutionary Computation, pages 139–148, Jerusalem, October 9–14, 1994. Springer, Berlin.
P. Sen and J.-B. Yang. Multipe Criteria Decision Support in Engineering Design. Springer, 1998.
E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne, editors. Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001), volume 1993 of Lecture Notes in Computer Science, Berlin, Germany, March 2001. Springer.
E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In K. Giannakoglou et al., editors, Evolutionary Methods for Design, Optimisation, and Control, 2002. To appear.
E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999.
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Laumanns, N., Laumanns, M., Kitterer, H. (2002). Evolutionary Multi-objective Integer Programming for the Design of Adaptive Cruise Control Systems. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_20
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DOI: https://doi.org/10.1007/3-540-48035-8_20
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