Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Abstract An experimental investigation of the effectiveness of ferrographic methods applied to diesel engine wear monitoring was carried out. A series of car engines running in service were considered and the wear phenomena were studied on the basis of total particle density and large particle concentration trends determined through ferrographic analysis of oil samples. Results have shown that ferrography can be successfully used in this specific application. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect. Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. An entire previous book by the same author [1] is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with GP to improve its efficacy on large problems. "An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically evolved during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved" (p. 1). Dr. Koza recommends adding the ADF technique to the "GP toolkit." The book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers. Main Points 3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be- <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> This paper compares and contrasts different types of neural network error bars in the context of a real world safety critical problem, specifically the problem is one of the calibration of engine management systems for air to fuel ratio and ignition timing tables. Three types of error bars are considered and developed for radial basis function networks and Gaussian processes. The different assumptions inherent in the error bars are discussed in the context of a synthetic problem, and then applied to off-line and on-line engine data. <s> BIB004 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> In earlier work published by the author and co-authors, a dynamic enginemodel called a Mean Value Engine Model (MVEM) was developed. This model isphysically based and is intended mainly for control applications. In itsnewer form, it is easy to fit to many different engines and requires littleengine data for this purpose. It is especially well suited to embedded modelapplications in engine controllers, such as nonlinear observer basedair/fuel ratio and advanced idle speed control. After a brief review of thismodel, it will be compared with other similar models which can be found inthe literature. The attempt will be made to point out the differencesbetween the new modified MVEM and those developed elsewhere. In particular,the questions of fitting simplicity and general applicability are to betreated. <s> BIB005 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> The mass production and wider use of automobiles and the incorporation of complex electronic technologies all indicate that the control of faults should be given an integral part of engine design and usage. Today, artificial intelligence (AI) technology is widely suggested for systematic diagnosis of faults where the amount of well-defined diagnosis knowledge is vast and the sequence of steps required to identify the fault is very long. This article describes on an expert system application for automotive engines. A new prototype named EXEDS (expert engine diagnosis system) has been developed using KnowledgePro, an expert system development tool, and run on a PC. The purpose of the prototype is to assist auto mechanics in fault diagnosis of engines by providing systematic and step-by-step analysis of failure symptoms and offering maintenance or service advice. The result of this development is expected to introduce a systematic and intelligent method in engine diagnosis and mai ntenance environments. <s> BIB006 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> From the Publisher: ::: Genetic algorithms (GA) and evolution strategies (ES) are relatively new stochastic based techniques for solving engineering problems on computers. GA and ES are based on a loose biological analogy: evolutionary theory (mutation, crossover, selection, survival of the fittest). Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. This book presents state of the art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. <s> BIB007 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Genesis motivation prediction experiments pattern recognition and classification control system design extension of early evolutionary programming concepts competitive goal-seeking some implications diversification two-person gaming against nonminimax players coevolution, pursuit and evasion modeling time series pattern recognition simulated ecosystems and the nature of intelligence sequence induction with deterministic automata revising and extending early evolutionary programming routing problems comparing crossover, inversion, and mutation specialisations finding structure in data self-adaptation evolving neural networks evolving S-expression and multiple interacting programs games other applications. <s> BIB008 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> List of Figures. List of Tables. Preface. Contributing Authors. Series Foreword. Part I: Foundations. 1. An Introduction to Evolutionary Algorithms J.A. Lozano. 2. An Introduction to Probabilistic Graphical Models P. Larranaga. 3. A Review on Estimation of Distribution Algorithms P. Larranaga. 4. Benefits of Data Clustering in Multimodal Function Optimization via EDAs J.M. Pena, et al. 5. Parallel Estimation of Distribution Algorithms J.A. Lozano, et al. 6. Mathematical Modeling of Discrete Estimation of Distribution Algorithms C. Gonzalez, et al. Part II: Optimization. 7. An Empiricial Comparison of Discrete Estimation of Distribution Algorithms R. Blanco., J.A. Lozano. 8. Results in Function Optimization with EDAs in Continuous Domain E. Bengoetxea, et al. 9. Solving the 0-1 Knapsack Problem with EDAs R. Sagarna, P. Larranaga. 10. Solving the Traveling Salesman Problem with EDAs V. Robles, et al. 11. EDAs Applied to the Job Shop Scheduling Problem J.A. Lozano, A. Mendiburu. 12. Solving Graph Matching with EDAs Using a Permutation-Based Representation E. Bengoetxea, et al. Part III: Machine Learning. 13. Feature Subset Selection by Estimation of Distribution Algorithms I. Inza, et al. 14. Feature Weighting for Nearest Neighbor by EDAs I. Inza, et al. 15. Rule Induction by Estimation of Distribution Algorithms B. Sierra, et al. 16. Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs L.M. de Campos, et al.17. Comparing K-Means, GAs and EDAs in Partitional Clustering J. Roure, et al. 18. Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms C. Cotta, et al. Index. <s> BIB009 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NL... <s> BIB010 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> We explain the biology and physics underlying the chemotactic (foraging) behavior of E. coli bacteria. We explain a variety of bacterial swarming and social foraging behaviors and discuss the control system on the E. coli that dictates how foraging should proceed. Next, a computer program that emulates the distributed optimization process represented by the activity of social bacterial foraging is presented. To illustrate its operation, we apply it to a simple multiple-extremum function minimization problem and briefly discuss its relationship to some existing optimization algorithms. The article closes with a brief discussion on the potential uses of biomimicry of social foraging to develop adaptive controllers and cooperative control strategies for autonomous vehicles. For this, we provide some basic ideas and invite the reader to explore the concepts further. <s> BIB011 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed. <s> BIB012 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and selection operators, LEM employs machine learning to generate new populations. Specifically, in Machine Learning mode, a learning system seeks reasons why certain individuals in a population (or a collection of past populations) are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to generate new populations. A remarkable property of LEM is that it is capable of quantum leaps (“insight jumps”) of the fitness function, unlike Darwinian-type evolution that typically proceeds through numerous slight improvements. In our early experimental studies, LEM significantly outperformed evolutionary computation methods used in the experiments, sometimes achieving speed-ups of two or more orders of magnitude in terms of the number of evolutionary steps. LEM has a potential for a wide range of applications, in particular, in such domains as complex optimization or search problems, engineering design, drug design, evolvable hardware, software engineering, economics, data mining, and automatic programming. <s> BIB013 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. Benchmark comparisons among the algorithms are presented for both continuous and discrete optimization problems, in terms of processing time, convergence speed, and quality of the results. Based on this comparative analysis, the performance of EAs is discussed along with some guidelines for determining the best operators for each algorithm. The study presents sophisticated ideas in a simplified form that should be beneficial to both practitioners and researchers involved in solving optimization problems. <s> BIB014 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Publisher Summary This chapter presents a new population-based search algorithm called the Bees Algorithm (BA). This algorithm mimics the food foraging behavior of swarms of honeybees. In its basic version, the algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization. This chapter focuses on the latter. Following a description of the algorithm, the chapter presents the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm. The results show that the algorithm can reliably handle complex multi-model optimization problems without being trapped at local solutions. One of the drawbacks of the algorithm is the number of tunable parameters used. However, it is possible to set the parameter values by conducting a small number of trials. <s> BIB015 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> An automobile assembly line is usually configured as three successive shops in which the body is constructed, painted, and then assembled together with all component parts into a finished vehicle. However, many published production sequencing models ignore the first two shops and base their results only on the requirements and constraints of the assembly shop. In this article, we propose to more closely follow the actual industrial structure. We therefore first propose a single objective mathematical model for scheduling the paint and assembly shops. We then propose an ACO metaheuristic for solving a multiple-objective formulation. Data provided by Groupe Renault show that the proposed approach offers better solutions than those of current practice. <s> BIB016 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach. <s> BIB017 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> The book contains basic concepts, several applications of Genetic Algorithms and solved Genetic Problems using MATLAB software and C/C++. The salient features of the book include - detailed explanation of Genetic Algorithm concepts, - numerous Genetic Algorithm Optimization Problems, - study on various types of Genetic Algorithms, - implementation of Optimization problem using C and C++, - simulated solutions for Genetic Algorithm problems using MATLAB 7.0, - brief description on the basics of Genetic Programming, - application case studies on Genetic Algorithm on emerging fields. <s> BIB018 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> This chapter contains sections titled: References Artificial Intelligence through a Simulation of Evolution Natural Automata and Prosthetic Devices <s> BIB019 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Abstract The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called “baseline.” However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second hand or have been purchased from unknown suppliers, meaning that the baseline is unknown. In this paper, a novel vibration-based fault diagnostic method is developed to identify the vital components of a diesel engine that have abnormal clearance. The advantage of this method is that it does not require the comparison of current operating parameters to those collected as the baseline. First, the nominal baseline is obtained via theoretical modeling rather than being actually captured from the sensory signals in a healthy condition. The abnormal clearance is then determined by inspecting the timing of impacts created by the components that had abnormal clearance during operation. To detect the timing of these impacts from vibration signals accurately, soft-re-sampling and empirical mode decomposition (EMD) techniques are employed. These techniques have integrated with our proposed ranged angle (RA) analysis to form a new ranged angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of the RA-EMD in detecting the impacts and their times of occurrence, their induced vibrations are collected from a series of generators under normal and faulty engine conditions. The results show that this method is capable of extracting the impacts induced by vibrations and is able to determine their times of occurrence accurately even when the impacts have been overwhelmed by other unrelated vibration signals. With the help of the RA-EMD, clearance-related faults, such as incorrect open and closed valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence, proper remedies can be applied to defective diesel engines to ensure that valuable fuel is not wasted due to the incorrect timing of combustion as well as unexpected fatal breakdown, which may cause loss of production or even human casualties, can be minimized. <s> BIB020 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE. <s> BIB021 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> In this paper a new evolving parameter estimation algorithm for a local model network under special consideration of combustion engine modelling is presented. For practical applications computational speed, incorporation of prior knowledge and the interpretability of the local models is of great interest. Accordingly, a robust and efficient online training algorithm with a particular focus on computational requirements involved in dynamic system identification of complex nonlinear processes is presented. The incremental construction of the model tree allows to gradually increase the model complexity while a proper initialisation of new model parameters is easily possible. The proposed evolving local model network is validated using real measurement data from a state-of-the-art 4-cylinder EURO5 diesel engine. <s> BIB022 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> M <s> Abstract The growing worldwide demand for more efficient and less polluting forms of energy production has led to a renewed interest in the use of micro-cogeneration technologies in the residential. Among the others technologies, internal combustion engine-based micro-cogeneration devices are a market-ready technology gaining an increasing appeal thanks to their high efficiency, fuel flexibility, low emissions, low noise and vibration. In order to explore and assess the feasibility of using internal combustion engine-based cogeneration systems in the residential sector, an accurate and practical simulation model that can be used to conduct sensitivity and what-if analyses is needed. A residential cogeneration device model has been developed within IEA/ECBCS Annex 42 and implemented into a number of building simulation programs. This model is potentially able to accurately predict the thermal and electrical outputs of the residential cogeneration devices, but it relies almost entirely on empirical data because the model specification uses experimental measurements contained within a performance map to represent the device specific performance characteristics coupled with thermally massive elements to characterize the device's dynamic thermal performance. At the Built Environment Control Laboratory of Seconda Universita degli studi di Napoli, an AISIN SEIKI micro-cogeneration device based on natural gas fuelled reciprocating internal combustion engine is available. This unit has been intensively tested in order to calibrate and validate the Annex 42 model. This paper shows in detail the series of experiments conducted for the calibration activity and examines the validity of this model by contrasting simulation predictions to measurements derived by operating the system in electric load following control strategy. The statistical comparison was made both for the whole database and the segregated data by system mode operation. The good agreement found in the predictions of net electric power production, useful thermal output and primary power consumption allowed to conclude that the Annex 42 model can be used to carry out a detailed performance assessment in order to examine the applicability of the AISIN SEIKI unit for supplying building electrical and thermal energy requirements according to different load profiles during annual or multi-year operation. <s> BIB023
ANY real-world problems can be formulated as optimization problems and many of them belong to the class of NP-hard problems  , implying that no efficient algorithms exist to find their exact global optima. This has therefore encouraged the researchers to develop new sets of algorithms including meta-heuristic algorithms, which are often inspired by nature. Among these are evolutionary algorithms (EAs) that emulate the idea of the survival of the fittest mechanism in Darwinian theory of evolution. The advantage of these algorithms is that they require little prior mathematical information about the problems they are to solve. Many population-based optimization algorithms  - BIB007 have emerged within the past few decades and have found their way into solving many optimization problems. One advantage of population-based optimization algorithms is the global search ability of the algorithms as the population consists of a number of individuals who are in cooperation with others search the solution space and share their knowledge about the problem BIB017 , BIB014 . These optimization algorithms, also known as meta-heuristic algorithms, can be categorized into two main groups, the EAs including genetic algorithms  , evolution strategy BIB012 , evolutionary programming BIB008 , genetic programming BIB002 , BIB003 , differential evolution  , BIB021 , estimation of distribution algorithm (EDA) BIB009 , and swarm intelligence algorithms including ant colony optimization  , particle swarm optimization (PSO)  , bees algorithms BIB015 , and bacterial foraging optimization BIB011 . Population-based algorithms also include memetic and cultural algorithms  , harmony search BIB010 , artificial immune systems  , and learnable evolution model BIB013 . Since proposed, these algorithms have been successfully adopted to solve many different problems in different areas from engineering to ecology to the social sciences BIB019 - BIB018 . Due to their great performance and applicability, meta-heuristic algorithms have been applied in many aspects of engine management systems. In this paper, we review the application of these algorithms in car engine management systems. The aim of this paper is to broadly cover applications such that a global view toward the state-of-theart on this topic can be obtained. The review also enables us to identify potential gaps in the literature and relevant future research directions. Many optimization problems arise when designing automobile engines BIB001 - BIB016 . Most of them involve multiple conflicting objectives. The goal in these kind of problems is no longer to find a single best solution, but rather a set of solutions representing the best trade-off among the objectives. In this respect, the goal in these problems is to identify a set of efficient (Pareto-optimal) solutions. One of the most important stages in engine design is engine calibration BIB004 - BIB023 , which is the adjustment or modification of an internal combustion engine or its control unit with the goal of achieving the optimal performance in terms of engine power, fuel economy, emissions, and durability. In other words, the problem is a multiobjective one with sometimes contradictory objectives. The calibration is performed on different parts of the engines including air intake, ignition timing, and valve timing. The engine control unit is an important part in an engine that controls a series of actuators with the goal of reaching the best performance. The control system reads values from a set of This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ sensors and, by interpreting the data using multidimensional performance maps, manages the engine actuators. There are a number of control systems in an engine, including air-tofuel-ratio control, idle speed control, start-up engine control, charge control, engine speed control, and fuel injection control. Optimizing the engine control systems is an important task in designing car engines. Engine fault diagnosis BIB006 - BIB020 is the act of finding faults arising in different parts of an engine. To find the faults in engines, some data about the engine condition are gathered and then processed to find the possible causes. Different methods are used to collect the data, including vibration monitoring, thermal imaging, and oil particle analysis. Then different algorithms are utilized to process these data, which include wavelet transform, wavelet analysis, Winger-Ville distribution, cepstrum, bi-spectrum, correlation methods, spectral analysis, and short-term Fourier transform. Many optimization problems occur in fault diagnosis of engine systems and a lot of research employs meta-heuristic algorithms when dealing with these problems. Another area of research in engine design is modeling different parts of the engines BIB005 - BIB022 . Modeling is performed for different reasons. One example is the design and optimization of a controller system. Designing a controller by using the real engine in the design process is an expensive and time-consuming task. Thus, many researchers first build a model of the engine and then design, test, and optimize their controller on this model. One other application of the models is for prediction, where the model of the engine is built and used to predict different behaviors of the engine in different environments. Optimizing different parts of an engine is yet another use of models, as optimizing the real engine directly is usually a time-consuming task, and it is easier to build a model and then optimize the parameters based on the engine model. Because of these wide ranges of applications of engine models, designing the best models is important and has been the focus of a lot of research. The rest of this paper is organized as follows. Section II describes applications of meta-heuristic algorithms in engine calibration. Section III reviews the algorithms in optimizing the control systems of engines. Applications of meta-heuristic algorithms in engine fault diagnosis are covered in Section IV. Meta-heuristic algorithms in optimizing different parts of engines are discussed in Section V. Section VI surveys the current work in engine modeling, and finally Section VII concludes this paper. Table I shows the structure of this paper.