Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 6) Prediction: <s> This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 6) Prediction: <s> Support Vector Regression (SVR) is proved to be a good and effective method for machine condition prediction. But prediction results are usually not satisfying for complex machines, e.g., a certain diesel engine. So a hybrid method GA-EMD-SVR is proposed in this paper integrating Genetic Algorithm (GA), Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR). The main ideal of the method is to select effective parameters combination from condition signal based on GA. Then EMD is employed to decompose the effective parameter sequences into several Intrinsic Mode Functions (IMFs) and a residual. Finally, each IMF and the residue are considered as training samples to train SVR based on space construction, whose model parameters is generated by using grid-search. Experiment data from a certain diesel engine is used to validate the model. Prediction results of single and multiple steps based on GA-EMD-SVR are validated to be feasible and more accuracy than GA-SVR model. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 6) Prediction: <s> The aim of this paper was to create and compare models of pressure in an injection pipe built by selected methods from the field of computational intelligence. Measurements of pressure in the injection pipe of a diesel engine on a test bench were conducted. Several models of the pressure in the injection pipe, including the proposed genetic-fuzzy system (GFSm) and the well-known neuro-fuzzy ANFIS method, were built. The weakest was the Takagi–Sugeno model created by genfis3 function. The models obtained by GFSm allow for the simulation of pressure for all allowable crankshaft speeds. The best model (built by GFSm) can be used to control and evaluate the quality of the injection process of piston engines fuelled by biofuel or diesel fuel with accuracy required in practical technical applications. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 6) Prediction: <s> Two of the major challenges of the European Union policy are to reduce both greenhouse gas (GHG) emissions and the dependence on fossil-based fuels. Biodiesel, produced through the transesterification of vegetable oils or animal fats, seems to be an excellent support nowadays, new raw materials to produce biodiesel are under study, provided that the production of biodiesel from edible oils is controversial for some social organizations. Olive pomace oil derives from the oil left in the olive fruit pulp (once olive oil has been extracted) and may be an option to considerer since it exhibits high neutral flavor that makes it undesirable for consumption, unless it is treated and further blended with virgin olive oil. Therefore, other uses for this oil could be soap production or its recycling to produce biodiesel. In this paper, noise emissions of a direct injection diesel engine Perkins fueled with olive pomace oil methyl ester (OPME) at several steady-state engine operating conditions were studied. This study proposes the use of different approaches for sound prediction of a diesel engine, based on artificial neural network (such as Evolutionary Product Unit Neural Networks (PUNNs) and Radial Basic Function Neural Networks (RBFNNs)) and response surface models. Their accuracies are compared in terms of Mean Square Error (MSE) and Standard Error of Prediction (SEP). It can be concluded that the use of PUNN improves the accuracy achieving acceptable values for both MSE and SEP by means of a simpler model than the combined PU and RBF NN proposed model. Moreover, it was found that the variable power does not explain the noise value prediction, the noise emitted by the engine is inversely related to the 1/3rd octave band of the frequency value and diesel fuel noise plays the most important role and influence in the PUNN model. Response surface models are rejected, due to their unacceptable accuracy in terms of noise prediction. <s> BIB004
SVMs are shown to provide good results in condition prediction. However, when it comes to complex systems, like diesel engines, the results are not very satisfying. Thus, a genetic algorithm was used in BIB002 to optimize the SVM in predicting the condition of a diesel engine. The genetic algorithm was used to select effective parameter combinations from the condition signal. Experimental data from a diesel engine were used to validate the model and showed good accuracy. In BIB004 , artificial neural networks and RBFs were used for sound prediction of a diesel engine. In this paper, the training algorithm for neural networks was an EA. 7) Pressure in Injection Pipe: Using computational intelligence methods, the pressure in an injection pipe of a diesel engine was modeled in BIB003 . Several methods including a genetic fuzzy system and neuro-fuzzy ANFIS were employed. The models were compared and the experimental analysis suggested that the best method is the genetic fuzzy system. Table II categorizes the reviewed papers based on the algorithms and applications. The most widely used algorithms are the genetic algorithm and PSO. This is because these two algorithms are easy to use and versatile. Other optimization algorithms like genetic programming, immune EAs, ant colonies, quantum EAs (QEA) BIB001 ,  , etc., have gained less attention. Each of these optimization problems are suitable for different problem types. The following observations can be made on the table. 1) GP is suitable for structure optimization problems. For problems like crankshaft design, cylinder design, piston design, valve design, etc., this algorithm is more likely to provide good results. 2) Ant colony optimization is suitable for graph-based optimization problems. Among the reviewed papers, only one has used the algorithm for a fault diagnosis problem, although the problem is not a graph-based problem. 3) QEAs are suitable for combinatorial and binary coded optimization problems. Many optimization problems in car engine design are combinatorial or binary coded, so considering QEA is suggested. 4) Many optimization problems in engine design involve uncertainty. This happens when noise is involved or when a fitness approximation method is used. Some algorithms like covariance matrix adaptation evolution strategy and EDAs can be more suitable for these sets of problems. 5) Hybridizing optimization algorithms should be performed carefully. That is the two algorithms should complement one another. For example, when a problem is partially binary and partially graph-based hybridizing QEA and ACO algorithm can be attempted. Since car engines are very complex, we are very likely to encounter these set of problems. 6) Memetic algorithms are not recommended for uncertain problems. When a problem involves uncertainty, the local search algorithms can easily be distracted as the fitness of the neighbors of the current solution may change or be inaccurate during the search process. Therefore, optimization algorithms that are more powerful at capturing the global behavior of the landscape are recommended. 7) The properties of the fitness landscape of the problem should also be taken into account. Problems with a more rugged landscape are better managed with global optimization algorithms, while the ones with more smooth landscapes and less number of local optima are more easily solved with memetic and local search algorithms.