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Hierarchical Adaptive Genetic Algorithm Based T-S Fuzzy Controller For Non-linear Automotive Applications

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第1期24卷 607-621页

作者： Abdelrahim, Elsaid Md Tanta Univ Fac Sci Tanta Egypt Northern Border Univ Fac Sci Ar Ar Saudi Arabia

In this paper, a robust and enhanced evolutionary computing assisted Takagi Sugeno (T-S) fuzzy controller was developed for automotive fuel injection control. To augment the rule generation and fuzzy parameter estimation, we propose an enhanced evolutionary computing algorithm named Hierarchical Adaptive Genetic Algorithm (HAGA). The proposed HAGA model was applied to augment T-S fuzzy controller that in conjunction with Fuzzy Clustering Mean (FCM) has enabled optimal rule generation and control parameter estimation. The proposed HAGA model exploits Hierarchical concept-based AGA implementation that itself embodies novelties like adaptive crossover and mutation probability, to enable accurate, swift, and efficient control function by T-S Fuzzy controller. These novelties strengthen the proposed HAGA-TS fuzzy controller to exhibit time efficient and accurate control function that could be of utmost significance for non-linear process control. The proposed controller design is examined for its efficacy over automotive or vehicle data containing fuel injection rate, throttle angle, emission products etc., where considering current emission and/or torque requirements, the throttle angle is varied to achieve environmentally friendly and cost-efficient vehicle design. The simulation results revealed that the proposed HAGA TS-fuzzy controller outperforms other state of art evolutionary computing-based approaches such as GA, PSO based T-S fuzzy controller.

关键词： Takagi-Sugeno fuzzy system Non-linear control Vehicle fuel injection control Torque control evolutionary computing Adaptive Genetic Algorithm

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Improved Levenberg-Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 5期38卷 3847-3869页

作者： Zhao, Jue Hoang Nguyen Trung Nguyen-Thoi Asteris, Panagiotis G. Zhou, Jian Hunan Univ Technol & Business Inst Big Data & Internet Innovat Changsha 410205 Peoples R China Hunan Univ Technol & Business Coll Comp & Informat Engn Changsha 410205 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece Cent South Univ Sch Resources & Safety Engn Changsha 410083 Hunan Peoples R China

The aim of this study is to develop a novel computer-aided method for the prediction of the deflection of reinforced concrete beams (DRCB) under concentrated loads. To this end, in the present work, a Levenberg-Marquardt-based backpropagation novel neural network model, optimized by the whale optimization algorithm (WOA), called WOA-LMBPNN, has been developed. Specifically, a neural network, using the Levenberg-Marquardt backpropagation training algorithm with multiple hidden layers, was optimized by the WOA, aiming to obtain higher accuracy in predicting DRCB. For the training of the models, 120 experiments with the geometrical and mechanical properties of concrete beams were compiled using were used as the input parameters. Seven datasets with different number of input variables were investigated to evaluate the effect of the input variables on DRCB. For comparison purposes, another swarm optimization algorithm (i.e., particle swarm optimization-PSO) was also used to optimize the LMBPNN model (i.e., PSO-LMBPNN model). The results obtained by the PSO-LMBPNN and WOA-LMBPNN models are then compared based on the different datasets. Finally, the results revealed the effective role of the WOA, as well as the efficiency and robustness of the new hybrid WOA-LMBPNN model in predicting DRCB.

关键词： Reinforced concrete beam evolutionary computing Artificial intelligence Smart structures Neural network computing Computer-aided method

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Proof of Swarm Based Ensemble Learning for Federated Learning Applications 23

Proof of Swarm Based Ensemble Learning for Federated Learnin...

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38th Annual ACM Symposium on Applied computing (ACM SAC)

作者： Raza, Ali Kim Phuc Tran Koehl, Ludovic Li, Shujun Univ Lille ENSAIT GEMTEX Genie & Mat Text ULR 2461 Lille France Univ Kent Inst Cyber Secur Soc iCSS Canterbury Kent England Univ Kent Sch Comp Canterbury Kent England

ISBN: (纸本)9781450395175

Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combine results of local models to produce a global model. Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications. This is because, in such methods predictions of some of the peers are disregarded, so a majority of peers can win without even considering other peers' decisions. Additionally, the confidence score of the result of each peer is not normally taken into account, although it is an important feature to consider for ensemble learning. Moreover, the problem of a tie event is often left un-addressed by methods such as BFT. To fill these research gaps, we propose PoSw (Proof of Swarm), a novel distributed consensus algorithm for ensemble learning in a federated setting, which was inspired by particle swarm based algorithms for solving optimisation problems. The proposed algorithm is theoretically proved to always converge in a relatively small number of steps and has mechanisms to resolve tie events while trying to achieve sub-optimum solutions. We experimentally validated the performance of the proposed algorithm using ECG classification as an example application in healthcare, showing that the ensemble learning model outperformed all local models and even the FL-based global model.

关键词： Privacy federated learning ensemble consensus protocol evolutionary computing swarm algorithms healthcare

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Introducing Learning Rate Adaptation CMA-ES into Rigid Intraoperative 2D/3D Registration for Spinal Surgery 22

Introducing Learning Rate Adaptation CMA-ES into Rigid Intra...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Chen, Minheng Zhang, Zhirun University of Texas Department of Computer Science and Engineering Arlington United States School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9798331520526

The covariance matrix adaptive evolution strategy (CMA-ES) has been widely used in the field of 2D/3D registration in recent years. This optimization method exhibits exceptional robustness and usability for complex surgical scenarios. However, due to the inherent ill-posed nature of the 2D/3D registration task and the presence of numerous local minima in the landscape of similarity measures, evolution strategies often require a larger population size in each generation to ensure the stability of registration and the globality and effectiveness of search, which makes the entire process computationally expensive. In this paper, we build a 2D/3D registration framework based on a learning rate adaptation CMA-ES manner. The framework employs a fixed and small population size, leading to minimized runtime and optimal utilization of computing resources. We conduct experimental comparisons between the proposed framework and other intensity-based baselines using a substantial volume of synthetic data. The results suggest that our method demonstrates superiority in both registration accuracy and running time compared with other optimization-based baselines. Code is available at ***/m1nhengChen/CMAES-reg. © 2025 IEEE.

关键词： 2D/3D registration CMA-ES evolutionary computing Image-guided interventions

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Mamdani Fuzzy-Based Assessment of Telework Capability of Philippine Government Employees

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2022年第6期26卷 905-913页

作者： Vicerra, Ryan Rhay P. Bandala, Argel A. Loresco, Pocholo James M. Gamara, Rex Paolo C. Mayol, Andres Philip Concepcion, Ronnie S., II Espanola, Jason E. San Juan, Jayne Lois G. Dadios, Elmer P. Culaba, Alvin De La Salle Univ Dept Mfg Engn & Management 2401 Taft Ave Manila 1004 Philippines De La Salle Univ Ctr Engn & Sustainabil Dev Res 2401 Taft Ave Manila 1004 Philippines De La Salle Univ Dept Elect & Comp Engn 2401 Taft Ave Manila 1004 Philippines FEU Inst Technol Dept Elect & Elect Engn P Paredes St Manila 1015 Philippines De La Salle Univ Dept Ind & Syst Engn 2401 Taft Ave Manila 1004 Philippines De La Salle Univ Dept Mech Engn 2401 Taft Ave Manila 1004 Philippines

Due to the advent of the COVID-19 pandemic, the Philippine government encouraged enterprises and businesses to utilize flexible work arrangements such as work-from-home (WFH) or telecommuting setup. Nowadays, the key components necessary for a telecommuting include a WiFi-enabled IT equipment, secured work environment, and reliable internet connection, while research shows that type of work and computer literacy are also key factors for telework implementation. Multiple studies in relation to telework have already been conducted but some studies were deemed inconclusive and need further analysis. Therefore, in this study, a Mamdani fuzzy-based model was developed for telework capability assessment for Philippine government employees based on four significant factors namely: internet speed, IT equipment availability, computer literacy, and type of work, which are expressed in linguistic representations. The proposed fuzzy system can provide a feedback telework capability score based on the four input parameters which may also be characterized with the potential telecommuting cost requirement.

关键词： computational intelligence expert system fuzzy systems evolutionary computing telework

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A Search for Nonlinear Balanced Boolean Functions by Leveraging Phenotypic Properties

A Search for Nonlinear Balanced Boolean Functions by Leverag...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Gasperov, Bruno Durasevic, Marko Jakobovic, Domagoj Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (纸本)9798400701207

In this paper, we consider the problem of finding perfectly balanced Boolean functions with high non-linearity values. Such functions have extensive applications in domains such as cryptography and error-correcting coding theory. We provide an approach for finding such functions by a local search method that exploits the structure of the underlying problem. Previous attempts in this vein typically focused on using the properties of the fitness landscape to guide the search. We opt for a different path in which we leverage the phenotype landscape (the mapping from genotypes to phenotypes) instead. In the context of the underlying problem, the phenotypes are represented byWalsh-Hadamard spectra of the candidate solutions (Boolean functions). We propose a novel selection criterion, under which the phenotypes are compared directly, and test whether its use increases the convergence speed (measured by the number of required spectra calculations) when compared to a competitive fitness function used in the literature. The results reveal promising convergence speed improvements for Boolean functions of sizes N = 6 to N = 9.

关键词： Boolean functions local search phenotype landscape Walsh-Hadamard Transform evolutionary computing mutation

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Identifying Self-excited Vibrations with evolutionary computing

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Procedia Computer Science 2014年 29卷 637-647页

作者： Christiaan D. Erdbrink Valeria V. Krzhizhano vskaya University of Amsterdam Amsterdam The Netherlands ITMO University Saint Petersburg Russia Deltares Delft The Netherlands Saint Petersburg State Polytechnic University Russia

This study uses differential evolution to identify the coeffic ients of second-order differentia l equations of self-e xc ited vibrations fro m a time signal. The motivation is found in the ample occurrence of this vibration type in engineering and physics, in particu lar in the real -life proble m of v ibrations of hydraulic structure gates. In the proposed method, an equation structure is assumed at the level of the ordinary differentia l equation and a population of candidate coefficient vectors undergoes evolutionary training. In this way the numerical constants of non-linear terms of various self-e xc ited vibration types were recovered fro m the time signal and the velocity value only at the initial t ime. Co mparisons are given regarding accuracy and computing time. Dependency of the test errors on the algorith m para meters is studied in a sensitivity analysis. The presented evolutionary method shows good promise for future applicat ion in engineering systems, in particular operational early -wa rning systems that recognise oscillations with negative damping before they can cause damage.

关键词： evolutionary computing system identification self-excited vibrations differential evolution

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Genetic Algorithm-Based Approach for Predicting Student Academic Success 24

Genetic Algorithm-Based Approach for Predicting Student Acad...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Mehdi, Riyadh Nachouki, Mirna Department of Information Technology Ajman United Arab Emirates

ISBN: (纸本)9798350384307

A student's academic success and on-time graduation, with all the social and economic rewards that come with that, can be significantly improved by predicting a student's performance and the factors contributing to his academic success. There is a limit on how much time an academic adviser can devote to each student to detect curriculum problems, take the appropriate steps, and give the student guidance based on well-informed judgment. Determining whether students are at risk early in the program is essential to improving low-performing students' performance, retention, and completion rates. This will give academic advisers early signals of the need for intervention. In this research, we built and assessed a model based on genetic algorithms to forecast student performance and estimate a student's graduation grade point average (GP A). The model was assessed using a publicly available dataset created for machine learning techniques with R2 = 0.93, indicating it did well. The model was also applied to a dataset made from academic records of students who graduated with a bachelor's degree in computing from our institution;the input predictors were the students' grades in core information technology courses in addition to their high school average and the dependent variable was the graduation grade point average. According to our research, the best predictor of graduation success is student performance in the database management systems course, followed by software engineering, with networking and operating system courses having minimal bearing. Additionally, the findings indicate that only 54% of the graduation grade point average can be explained by the predictors used;hence, other academic and sociodemographic factors will need to be considered in future studies. © 2023 IEEE.

关键词： computing education Data mining evolutionary computing Genetic Algorithms Student success prediction

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Fuzzy Logic Applications in Computer Science and Mathematics 1

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2023年

作者： Rahul Kar Dac-Nhuong Le Gunjan Mukherjee Biswadip Basu Mallik Ashok Kumar Shaw

ISBN: (数字)9781394175130

ISBN: (纸本)9781394174539

FUZZY LOGIC APPLICATIONS IN COMPUTER SCIENCE AND MATHEMATICSTICS The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering. The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes. Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The application-oriented approach is the unique feature of this book. Audience This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.

关键词： Fuzzy logic machine learning decision system cognitive computing multi-attribute decision making risk management quality control game application clustering classification multivariable control text mining data mining video frames image retrieval neuro-fuzzy Typoe-2 fuzzy logic control evolutionary computing

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Micro evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic

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SYSTEMS AND SOFT computing 2023年 5卷

作者： Solano-Rojas, Braulio J. Villalon-Fonseca, Ricardo Batres, Rafael Univ Costa Rica ECCI CITIC Ciudad Univ Rodrigo FacioSan Pedro Montes De Oca San Jose 11501 Costa Rica Tecnol Monterrey Prolongac Ezequiel Montes Santiago De Queretaro 76140 Queretaro Mexico

This work introduces the algorithm Micro evolutionary Particle Swarm Optimization (MEPSO), which is based on the Particle Swarm Optimization algorithm (PSO). MEPSO's main idea is to replace PSO's update of velocity with evolutionary mutations and crossovers that occur probabilistically. MEPSO uses two control hyperparameters of the evolutionary operators, alpha and beta, to improve convergence ability. These hyperparameters allow the algorithm to adapt to different problems. MEPSO algorithm has a crucial feature: the use of micropopulations. This results in reduced computational complexity, faster convergence rate, prevention of premature convergence, and easy implementation. The proposed approach provides the basis for further study of MEPSO's suitability for dynamic and multiobjective optimization. We evaluated MEPSO and PSO against 14 chosen benchmark functions. PSO's results revealed that it had an average success rate of 84% and a median computation time of 49.9 s. MEPSO, on the other hand, had a success rate of 81% and a computation time of 8 s. We found MEPSO to be faster when we performed a right-tail Wilcoxon Signed-Rank statistical test against the Mean Computation Time of 30 runs for each function for each algorithm.

关键词： Swarm intelligence Modified particle swarm optimization algorithm evolutionary computing

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