Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine

Estimation of biochemical variables using quantumbehaved particle swarm optimization (QPSO)-trained radius basis function neural network: A case study of fermentation process of L-glutamic acid

Due to the difficulties in the measurement of biochemical variables in fermentation process, softsensing model based on radius basis function neural network had been established for estimating the variables. To generate a more efficient neural network estimator, we employed the previously proposed quantum-behaved particle swarm optimization (QPSO) algorithm for neural network training. The experiment results of L-glutamic acid fermentation process showed that our established estimator could predict variables such as the concentrations of glucose, biomass and glutamic acid with higher accuracy than the estimator trained by the most widely used orthogonal least squares (OLS). According to its global convergence, QPSO generated a group of more proper network parameters than the most popular OLS. Thus, QPSO-RBF estimator was more favorable to the control and fault diagnosis of the fermentation process, and consequently, it increased the yield of fermentation.Key words: Soft-sensing model, quantum-behaved particle swarm optimization algorithm, neural network

Bibliometric of Feature Selection Using Optimization Techniques in Healthcare using Scopus and Web of Science Databases

Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is built using the feature selection algorithm. The solitary motive of the research paper analysis is to comprehend the reach and utility of optimization algorithms such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO) in the field of Health care. The aim is to bring efficiency and maximum optimization in the health care sector using the vast information that is already available related to these fields. With the help of data sets that are available in the health care analysis, our focus is to extract the most important features using optimization techniques and work on different algorithms so as to get the most optimized result. Precision largely depends on usefulness of features that are taken into consideration along with finding useful patterns in those features to characterize the main problem. The Performance of the optimized algorithm finds the overall optimum with less function evaluation. The principle target of this examination is to optimize feature selection technique to bring an optimized and efficient model to cater to various health issues. In this research paper, to do bibliometric analysis Scopus and Web of Science databases are used. This bibliometric analysis considers important keywords, datasets, significance of the considered research papers. It also gives details about types, sources of publications, yearly publication trends, significant countries from Scopus and Web of Science. Also, it captures details about co-appearing keywords, authors, source titles through networked diagrams. In a way, this research paper can be useful to researchers who want to contribute in the area of feature selection and optimization in healthcare. From this research paper it is observed that there is a lot scope for research for the considered research area. This kind of research will also be helpful for analyzing pandemic scenarios like COVID-19

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net

This study proposes and applies a comprehensive learning particle swarm optimization (CLPSO) fuzzy Petri net (FPN) algorithm, which is based on the CLPSO algorithm and FPN, to the fault diagnosis of a complex motor. First, the transition confidence is replaced by a Gaussian function to deal with the uncertainty of fault propagation. Then, according to the Petri net principle, a competition operator is introduced to improve the matrix reasoning. Finally, a CLPSO-FPN model for motor fault diagnosis is established based on the motor failure mechanism and fault characteristics. The CLPSO algorithm is used to generate the system parameters for fault diagnosis and to improve the adaptability and accuracy of fault diagnosis. This study considers the example of a three-phase asynchronous motor. The results show that the proposed algorithm can diagnose faults in this motor with satisfactory adaptability and accuracy compared with the traditional FPN algorithm. By establishing the system model, the fault propagation process of motors can be accurately and intuitively expressed, thus improving the fault treatment and equipment maintenance of motors

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Analysis of Fault Detection in Analog Circuits Using WSF-SKC Optimized SVM Technique

Many industrial applications and control systems depend heavily on analogue electrical circuitry. The conventional method of diagnosing such circuit faults can be time-consuming and erroneous, which might have a severe impact on the industrial output. The fault detection and analysing of analogue circuits with intelligent effective model is proposed in this work. The suggested technique primarily consists of two main stages one is extraction of features and the other is classification of faults. The analysis is performed on the response of frequency in analogue circuits. For extracting features particle swarm optimization (PSO) is utilized. The PSO is used to evaluate the fitness function of Wilks A-Statistic Filters sallen-key circuit (WSF-SKC). With fault characteristics retrieved using the particle swarm approach that are carefully selected, the fault classes may be separated more quickly. To categorise different failures in a benchmark circuit, a Support Vector Machine (SVM) classifier is built. Utilising firefly optimisation, the classifier is improved. Different fault codes were tested in experiments for defect detection and identification. The findings of the experiment indicate that this proposed technique can significantly increase the accuracy of fault diagnosis. The accuracy obtained for WS-LPF is 99.95%, WS-HPF is 99.97 and WS-BPF is 99.90% respectively