Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> III. CONTROL <s> In the present paper, online optimiza­ tion of an engine controller by means of Genetic Algorithms (GA) is discussed. In optimization of real complex systems through experiments and computer simulation using randolll variables, op­ timization methods must cope with uncertainty of objective function and limitation of possible number of evaluation. Sano et a1. have pro­ posed a GA utilizing history of search (GA with Memory-based Fitness Evaluation: MFEGA)(l) so as to reduce number of fitness evaluation for such applications of GA. In the proposed method, value of fitness function at a novel search point is estimated not only by the sampled fitness value at that point but also by utilizing the fitness val­ ues of individuals stored in the history of search. In the present paper, this method is applied to online optimization of an engine controller for ve­ hicles. Computer experiments using an engine simulator show that the proposed method out­ performs conventional GAs both in convergence speed and accuracy of solution under fluctu ation of fitness evaluation. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> III. CONTROL <s> In today’s combustion engine design a multitude of electronically influenced controlling parameters emerges. Accordingly, the quantity and complexity of control and regulation tasks assigned to the engine control unit arises. Moreover, a growing number of generally conflicting requirements have to be taken into account such as the reduction of fuel consumption with simultaneous consideration of best possible engine performance. Hence, the goal of development and calibration of an engine control unit is to provide optimized settings for the control variables of a specific working point in view of one objective function or even several objectives to be considered. Due to the vast complexity of this optimization task, conventional optimization approaches are inapplicable. Furthermore, due to the large search space, the verification of various possible solutions at the engine test bed is costly and not practicable. Thus, genetic algorithms can be a promising approach not supplying any further information on the system under optimization as evolutionary steps are based on a computable engine model rather than the real engine test bed. Here, the basic applicability of this approach is initially investigated by means of a particular engine model and a fixed working point. Different settings for genetic algorithms are evaluated for this task and the efficiency and scalability is discussed. Furthermore, the intermediate results collected during optimization can be used to derive a simplified engine model by identifying additional relations between variables. Finally, the results determined are verified on the real engine test bed. <s> BIB002
A growing number of conflicting requirements on vehicle engines, like fuel consumption, emission, and engine performance, constantly increase the complexity of control and regulation tasks in engine control unit design. This vast complexity makes the conventional optimization approaches inapplicable and thus new sets of methods are required to solve these problems. Meta-heuristic algorithms have been used in solving optimization problems of engine control systems. Different algorithms were applied to different problems in the control unit design. In this section, we review such research. In designing engine controller systems, the optimization methods must cope with uncertainties in the objective function and a limited number of fitness evaluations. To reduce the number of fitness evaluations in optimizing an engine control system, a new genetic algorithm was proposed by Sano et al. BIB001 , which utilizes the history of search. In this method, the value of fitness function at a novel search point was estimated by the sampled fitness value at the point and by utilizing the fitness values of individuals stored in the history of search. The authors applied their method to an engine simulator and showed that their proposed method outperformed conventional genetic algorithms. Optimizing the control system of a particular engine model at a fixed working point via genetic algorithms was performed by Sun et al. BIB002 , where different genetic algorithms were employed and compared in solving the problem. The authors tested their algorithm on a real engine.