Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> E. Diesel Engines <s> The European standards concerning the pollutants emissions of automotive engine become more and more severe. Modern automotive engines are equipped with an increasing number of new technologies and controlling elements. The consequence of this evolution, is the increase of the number of the controllable parameters, the difficulty to understand the engine behavior, and to find the parameters settings that offer the best compromise across the entire engine map, especially between fuel consumption and emissions constraints. This paper deals with problem of engine calibration, using the minimum of experiments. The approach proposed consists in building a global emulator based on Kriging model, which was adapted to take into account a number of control parameters greater than 3, while existing software are limited to two control parameters. This model is used to predict an engine response, and is coupled with a genetic algorithm, in order to give a best setting of parameters, optimizing the fuel consumption within constraints on the emission of NO∞ (nitrogen oxide). The main advantage of this approach is, its capacity to take into account a considerable number of controllable parameters in the optimization process, without lost in accuracy of model prediction. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> E. Diesel Engines <s> A novel process has been developed for reducing complexity in real-world, high-dimensional, multi-objective optimisation problems. This approach relies on being able to identify and exploit local harmony between objectives to reduce dimensionality. To achieve this, a systematic and modular process has been designed to cluster the Pareto-optimal front and apply a rule-based Principal Component Analysis including preference articulation for potential objective reduction. This many-objective optimisation decision-making process is demonstrated on a real-world, automotive diesel engine calibration optimisation problem comprising six objectives. The complexity reduction process resulted in three- and four-objective sub-problems. In the former, a significant improvement was achieved in one of the retained objectives at very little cost to the others. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> E. Diesel Engines <s> We present two new approaches to address the optimization problem associated with engine calibration. In this area, the tuning parameters are traditionally determined in a local way, i.e., at each engine operating point, via a single-objective minimization problem. To overcome these restrictions, the first method we propose is able to cope with several objective functions simultaneously in the local formulation. The second method we put forward relies on a global formulation, which allows the whole driving cycle to be taken into account while remaining single-objective. At the practical level, the two methods are implemented by combining various existing techniques such as the LoLiMoT (Local Linear Model Tree) parameterization and the MO-CMA-ES (Multi-Objective Covariance Matrix Adaptation Evolution Strategy) algorithm. A better compromise appears to be achieved on real case applications. <s> BIB003
Engine calibration is also an important step in designing diesel engines. Similar to calibration in gasoline engines, the optimization cycle in diesel engines is described by engine maps. The difficulties associated with the engine map design and engine calibration were described in  and BIB003 and a MOEA was proposed. The authors applied their method to a real data set obtained from a diesel engine in their automated test-bench. The calibration of a diesel engine was addressed in  where the parameters of an engine were tuned to optimize the emission and fuel efficiency. In order to optimize the engine, a neural network model of the engine was developed over a wide range of the engine operations. Then using the model, the parameters of the engines are optimized using a MOEA. To calibrate a diesel engine, a global emulator-based Kriging model of the engine was used by Brahmi et al. BIB001 to predict the engine response, and then a genetic algorithm was adopted to give the best setting of parameters and optimize the fuel consumption within constraints of the NO x emission. The authors believed that the main advantage of their method was its capacity to take into account a considerable number of controllable parameters without sacrificing the accuracy of the model prediction. In order to reduce the complexity of the objectives in the calibration process, a new method was proposed in BIB002 , which identifies and exploits local harmony between the objectives to reduce the number of objectives. In this method, a systematic process was designed to cluster the Pareto-optimal front and apply a rule-based principal component analysis for objective reduction. They applied this method to a diesel engine calibration optimization problem with six objectives and resulted in three-and four-objective sub-problems. Applying this method, a significant improvement was reported in one of the retained objectives at very little cost to the others.