Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> F. Monitoring <s> We propose a method for engine configuration diagnostics based on clustering of engine parameters. The method is tested using simulation of PID controller parameters generated and selected using a genetic algorithm. The parameter analysis is based on a state-of-the art method using multivariate extreme value statistics for outlier detection. This method is modified using a variational mixture model which automatically defines a number of Gaussian kernels and replaces a Gaussian mixture model. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> F. Monitoring <s> In practical question, the standard to judge whether an engine performs properly is fuzzy, as it’s difficult to give a critical value definitely. According to the engine condition and the complexity, uncertainty and nonlinear of the corresponding parameters, this paper proposed boundary optimization gradient genetic algorithm. In gradient genetic algorithm, BP algorithm of local search is introduced. The adaptive value of chromosomes group gets quickly improved with the search in one coding field getting avoided due to the utilization of knowledge of chromosomes in problem-domain. The crossover and mutation operations are added so that chromosomes will not fall into the local minimum point in neighborhood. Experiment indicates gradient genetic algorithm is a fast algorithm that can support the local optimization of individual chromosome and the global optimization of chromosomes in a group. <s> BIB002
The importance of many internal combustion engines whose work condition affects the performance of a car, makes the monitoring of the engines an effective way to prevent the costly faults. As the vibration signals from a diesel engine reflect the condition of the engine, they can be used by pattern recognition algorithms to monitor engine conditions. In  , the vibration signals from a diesel engine were captured and the characteristic features of the signals were extracted in the amplitude, time, and frequency domains to form the features. Then, a genetic algorithm was employed to find effective features which were more representative of different conditions of the engine and to reduce the feature space dimension. The authors used self-organization feature mapping as the pattern classifier. In BIB001 , genetic algorithms were employed in an engine configuration monitoring task, where visualization of parameter sets for PID controllers, data clustering, and detection of outliers were used. The method was tested by statistically generating parameters for PID through a genetic algorithm. To design the fitness function, an ideal reference signal was considered, then the difference between the output signal and the ideal signal was set to be the error. Another example of the applications of meta-heuristic algorithms in condition monitoring of an engine is presented in BIB002 , where a boundary optimization gradient genetic algorithm was employed. Fault diagnosis is an important problem that arises in many aspects of any type of car engines. Due to its complexity, many different techniques including neural networks, SVMs, and fuzzy systems have been developed. To optimize these methods, different meta-heuristic optimization techniques have been employed that were covered in this section.