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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines
verfasst von : Kosmas Knödler, Jan Poland, Peter Merz, Andreas Zell
Erschienen in: Recent Advances in Memetic Algorithms
Verlag: Springer Berlin Heidelberg
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Many combinatorial optimization problems occur in the calibration of modern automotive combustion engines. In this contribution, simple hill-climbing algorithms (HCs) for three special problems are incorporated in Memetic Algorithms (MAs) using specific crossover and mutation operators. First, the
k
-exchange algorithm as a well known technique of
D
-optimal design of experiments (DOE) is improved. Second, a (near-)optimum test bed measurement scheduling (TBS) as a variant of the traveling salesman problem (TSP) is calculated, and third, the final design of look-up tables (LTD) for electronic control units is optimized. It is shown that in all cases MAs that work on locally optimal solutions calculated by the corresponding HCs significantly improve former results using Genetic Algorithms (GAs). The algorithms have been successfully applied at BMW Group Munich.