Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 4) Fuel Economy in Bio-Diesel Engines: <s> Abstract This paper addresses artiﬁcial neural network (ANN) modeling followed by multi-objective optimization process to determine optimum biodiesel blends and speed ranges of a diesel engine fueled with castor oil biodiesel (COB) blends. First, an ANN model was developed based on standard back-propagation algorithm to model and predict brake power, brake speciﬁc fuel consumption (BSFC) and the emissions of engine. In this way, multi-layer perception (MLP) network was used for non-linear mapping between the input and output parameters. Second, modiﬁed NSGA-II by incorporating diversity preserving mechanism called the e-elimination algorithm was used for multi-objective optimization process. Six objectives, maximization of brake power and minimization of BSFC, PM, NO x , CO and CO 2 were simultaneously considered in this step. Optimization procedure resulted in creating of non-dominated optimal points which gave an insight on the best operating conditions of the engine. Third, an approach based on TOPSIS method was used for ﬁnding the best compromise solution from the obtained set of Pareto solutions. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 4) Fuel Economy in Bio-Diesel Engines: <s> This study aims to determine optimal biodiesel ratio that can achieve the goals of fewer emissions, reasonable fuel economy and wide engine operating range. Different advanced machine learning techniques, namely ELM (extreme learning machine), LS-SVM (least-squares support vector machine) and RBFNN (radial-basis function neural network), are used to create engine models based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints. A case study is presented to verify the modeling and optimization framework. Moreover, two comparisons are conducted, where one is among the modeling techniques and the other is among the optimization techniques. Experimental results show that, in terms of the model accuracy and training time, ELM with the logarithmic transformation is better than LS-SVM and RBFNN with/without the logarithmic transformation. The results also show that PSO outperforms SA in terms of fitness and standard deviation, with an acceptable computational time. <s> BIB002
Determining the optimal bio-diesel blends has a great deal of effect on the fuel economy and emissions of a bio-diesel engine. In order to find the best bio-diesel blend and speed ranges of a diesel engine, a neural network was first developed in BIB001 to model and predict brake power, BSFC, and the emissions of engine. Then a NSGA in conjunction with a diversity preserving mechanism called the -elimination algorithm was used to perform the optimization process on the model of the engine. The authors considered six different objectives and used a TOPSIS-based method to find the best solution. In BIB002 , the optimal bio-diesel ratio with the goal of achieving fewer emissions and reasonable fuel economy was found. Using different advanced machine learning methods including extreme learning machines, least-square SVMs, and radial-basis function neural networks, a model of the engine was generated and then subject to different constraints and using simulate annealing and PSO to optimize the best bio-diesel ratio.