Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Modeling Gasoline Engines 1) Sensor Systems: <s> An accurate mathematical model is a useful tool for analysis and design of sensor systems. In this paper, a novel approach based on particle swarm optimization (PSO) is applied to determine the parameters of dynamic sensor model. A hot-film mass airflow (MAF) sensor, which is used to measure the intake mass airflow in the engine systems of automobile, has been used as a simulation example for demonstration. The results indicate that the PSO is an effective technique for determining the parameters of dynamic sensor models. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Modeling Gasoline Engines 1) Sensor Systems: <s> This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this study is to develop an alternative robust formulations based on experimental data and to verify the use of GP for generating the formulations for gasoline engine torque and brake specific fuel consumption. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. Considerable good performance was achieved in predicting gasoline engine torque and brake specific fuel consumption by using GP. The performance of accuracies of proposed GP models are quite satisfactory (R2Â =Â 0.9878 for gasoline engine torque and R2Â =Â 0.9744 for gasoline engine brake specific fuel consumption). The prediction of proposed GP models were compared to those of the neural network modeling, and strictly good agreement was observed between the two predictions. The proposed GP formulation is quite accurate, fast and practical. <s> BIB002
A hot-film mass air-flow system sensor used in automobile engines to measure the intake mass airflow was studied in BIB001 . In this method, a novel approach for modeling the sensor system was proposed and PSO was proposed to optimize the model. The experiments performed showed the effectiveness of PSO in finding the parameters of dynamic sensor models. 2) Prediction: In order to predict the torque and BSFC of a gasoline in terms of spark advance, throttle position and engine speed, a genetic programming-based model of the engine was developed in BIB002 . Experimental data were gathered and used to train and test the system. The results presented in this paper suggested that the proposed method was comparable to neural network modeling systems in terms of speed, and accuracy.