Since multi-objective optimizations are becoming more important in engine calibration, the paper investigates multi-objective genetic algorithms in the application of engine optimization. Although several multi-objective genetic algorithms have been developed and some have been applied successfully in the automotive industry, it is difficult to determine which multi-objective genetic algorithm outperforms others in finding the set of optimal solutions or Pareto-optimal front in a practical multi-objective optimization problem. Based on some widely used multi-objective genetic algorithms, the paper proposes a combined scheme to deal with the difficulties in finding the optimal solution set during the engine calibration process. In the proposed approach the real-coded representation is employed in the genetic algorithm and the elitist strategy is applied for each multi-objective genetic algorithm used. To assess the proposed approach, two computational examples are given to minimize the brake specific fuel consumption and to maximize the output power torque simultaneously. The results show that the proposed approach is well suited to multi-objective optimization in engine calibration.


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    Titel :

    Investigation of a multi-objective optimization tool for engine calibration


    Beteiligte:
    Wu, M H (Autor:in) / Lin, W (Autor:in) / Duan, S Y (Autor:in)


    Erscheinungsdatum :

    2008-02-01


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch