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multi-objective genetic algorithm for Optimizing an ELM-Based Driver Distraction Detection System

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11946-11959页

作者： Echanobe, Javier Basterretxea, Koldo del Campo, Ines Martinez, Victoria Vidal, Naiara Univ Basque Country UPV EHU Dept Elect & Elect Bilbao 48940 Vizcaya Spain

Driver Assistance Systems (DAS) have been progressively incorporated into commercial vehicles in recent years. All these systems are paving the way for the forthcoming autonomous vehicle which will become a reality in the near future. Existing systems are based on numerous electronic systems with advanced skills, high performances, and high degrees of adaptability and intelligence. As is to be expected, these cutting-edge features require, in most cases, the use of powerful computing platforms. However, the deployment of such platforms is not an easy task, since they have to be integrated in the vehicle where there exist important restrictions regarding size, power consumption and cost. In this sense, every smart proposal aimed at reducing the complexity of these systems without degrading performance, is always a valuable contribution in the field. In this work, we propose a methodology to reduce the dimensionality of a driver distraction recognition system. The methodology is based on a multi-objective genetic algorithm that looks for the minimum set of useful features collected during the driving task and also for the simplest recognition system. The recognition algorithm is an Extreme Learning Machine (ELM) whose simplicity and fast learning procedure make it especially suitable to be used by a genetic algorithm which needs to evaluate thousands of candidate solutions. The proposed methodology has been tested with a real-world database collected from different drivers performing an itinerary with an instrumented car. The results obtained validate the proposal as a method to reduce the complexity of a driver distraction recognition system.

关键词： Vehicles Task analysis Visualization Proposals Feature extraction Automobiles genetic algorithms DAS driver distraction ELM multi-objective genetic algorithm feature selection

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Optimizing TRMS stability: a multi-objective genetic algorithm approach to PID controller design

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ENGINEERING COMPUTATIONS 2025年第2期42卷 710-721页

作者： Celebi, Baris Bilgic, Bogac Istanbul Univ Cerrahpasa Dept Mech Engn Istanbul Turkiye

PurposeThis paper aims to enhance the stability and control of twin rotor multi-input multi-output system (TRMS) helicopters by introducing a novel approach that utilizes a multi-objective genetic algorithm (MOGA) for optimizing proportional, integral, derivative (PID) controllers in simultaneous pitch and yaw ***/methodology/approachThe TRMS, a common prototype for helicopter motion studies, is introduced, and a PID controller is designed for pitch and yaw stabilization. The gains of the PID controller are optimized using a MOGA, a technique not previously proposed for TRMS in the *** various controllers have been explored in literature for TRMS stabilization, a MOGA-optimized PID controller for TRMS has not been proposed before. Simultaneous optimization of both pitch and yaw motions using two PID controllers is expected to yield improved *** limitations/implicationsThe study focuses on simulations, and experimental validation is not conducted. The MOGA is introduced as an optimization technique, and future studies may explore its application in experimental ***/valueThis study introduces a novel approach by utilizing a MOGA to optimize PID controller gains for TRMS. Simultaneous optimization of pitch and yaw motions aims to enhance robustness, providing a unique contribution to the field of helicopter control.

关键词： Twin Rotor multi-Input multi-Output System PID controller multi-objective genetic algorithm Helicopter control Motion stabilization

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Structural optimization of mining decanter centrifuge based on response surface method and multi-objective genetic algorithm

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2025年 212卷

作者： Cong, Peichao Zhou, Dong Li, Wenbin Deng, Murong Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Peoples R China

A significant quantity of slime water generated during coal mining poses a serious threat to the health of underground workers and the environment. The decanter centrifuge is widely employed in slime water treatment due to its high efficiency in solid-liquid separation. This paper proposes a structural optimization framework for the mine decanter centrifuge based on the Response Surface Method (RSM) and multi-objective genetic algorithm (MOGA). Firstly, a three-dimensional numerical model of the decanter centrifuge was established, and the reliability of the model was verified by experimental and theoretical analysis. Subsequently, the Box-Behnken design method and RSM were employed to construct a response surface model that links input parameters (drum half cone angle, screw pitch, and spiral blade Inclination angle) with target variables (solid phase recovery rate and overflow liquid phase solids content). The interactions between each input parameter and target variable were assessed using analysis of variance (ANOVA), which confirmed the model's effectiveness and generalization capability. Finally, MOGA was employed to optimize the centrifuge's structural parameters, resulting in an 8.16 % increase in solid recovery rate and a 35.84 % reduction in overflow liquid solid content. It offers a valuable reference for the structural optimization of decanter centrifuges in coal slurry separation.

关键词： Decanter centrifuge Response surface methodology multi-objective genetic algorithm Structural optimization

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Method research on equilibrium cycle reloading pattern optimization for the HPR1000 reactor core using deep learning-enhanced multi-objective genetic algorithm

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ANNALS OF NUCLEAR ENERGY 2025年 220卷

作者： Butt, Muhammad Kamran Wan, Chenghui Wei, Luo Khan, Izat Cao, Liangzhi Xi An Jiao Tong Univ Sch Nucl Sci & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

A novel methodology for designing and optimizing a PWR's equilibrium cycle reloading pattern using deep learning and a multi-objective genetic algorithm (MOGA) has been developed. The deep-learning model efficiently and accurately predicted reactor physics parameters, particularly fuel assembly burnups at the end of the cycle (EOC), and formed a fitness function. The fitness function takes the absolute difference between the conformable fuel assemblies' burnups at the beginning of the cycle (BOC) and the EOC, which narrows down the potential equilibrium reloading patterns. The deep-learning model was coupled with MOGA, which simultaneously optimized multiple objectives for the design and optimization of an equilibrium cycle. Applied to the HPR1000 reactor, the method achieved the first equilibrium cycle length of 473.1 Effective Full Power Days (EFPDs) and an average of 471.1 EFPDs over ten cycles, meeting all the parameters of reactor safety design criteria.

关键词： Deep-learning algorithm multi-objective genetic algorithm Equilibrium-cycle optimization HPR1000

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A Q-learning-based improved multi-objective genetic algorithm for solving distributed heterogeneous assembly flexible job shop scheduling problems with transfers

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JOURNAL OF MANUFACTURING SYSTEMS 2025年 79卷 398-418页

作者： Yang, Zhijie Hu, Xinkai Li, Yibing Liang, Muxi Wang, Kaipu Wang, Lei Tang, Hongtao Guo, Shunsheng Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

With the advancement of economic globalization, the distributed heterogeneous factory environment has become the mainstream in manufacturing enterprises. Scheduling flexible job shops in such a production environment holds practical value. However, due to the high complexity of certain jobs, the transfer of jobs between different factories are often required in practical production to balance machine load rates. Accordingly, this study addresses the distributed heterogeneous assembly flexible job shop scheduling problem with transfers, aiming to minimize both the makespan and total energy consumption. First, a multi-objective optimization model is formulated to define the problem, wherein knowledge of factory assignment and processing sequence for operations is summarized. Subsequently, given the complexity of this problem, a Q-learning-based improved multi-objective genetic algorithm (QL-IMOGA) is proposed as an effective approach. Within the proposed algorithm, a hybrid population initialization method is designed, considering factory load balancing and the earliest product completion time, to generate a high-quality initial population. Furthermore, two types of crossover operators, four types of mutation operators, and six objective-oriented neighborhood search operators are devised to enhance the algorithm's exploration and exploitation capabilities. Q-learning is employed for adaptive adjustment of key parameters to improve both convergence speed and solution quality. The effectiveness of the proposed population initialization method and neighborhood search operators is validated through 15 test cases. The results demonstrate that the proposed algorithm significantly outperformed four advanced meta-heuristic algorithms. Furthermore, it is observed that the solution employing the job transfer strategy led to an average reduction of 7.5% in makespan, a 3.9% decrease in total energy consumption, and an 8.4 % improvement in factory load rates compared to the soluti

关键词： Q -learning-based multi-objective genetic algorithm Distributed heterogeneous assembly Job transfer Flexible job shop scheduling

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Optimization of ball mill cylinder structure based on response surface optimization module and multi-objective genetic algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第7期38卷 3631-3640页

作者： Shang, Yunyue Shen, Jicheng Wei, Weihua Zheng, Bo Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

The cylinder mass affects the ball mill's operation and economy. To reduce the mass of the current cylinder, this paper utilized a response surface optimization module and a multi-objective genetic algorithm to optimize the cylinder structure. First, the optimal parts of the cylinder were determined by static analysis. Subsequently, the mathematical model was established with the goal of achieving structural lightweight. Then, the size of the cylinder was optimized based on the response surface optimization module and multi-objective genetic algorithm. Finally, the static analysis and modal analysis results before and after optimization were compared to verify the stress and resonance of the structure, and the optimization effect was evaluated. The optimized cylinder had a mass reduction of 15.624 % without compromising the strength and deformation. The optimization design can save materials and costs, and provide a theoretical reference for the lightweight research of the ball mill's cylinder.

关键词： Ball mill cylinder Response surface optimization module multi-objective genetic algorithm Finite element analysis Structure optimization

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multi-objective genetic algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2021年第5期41卷 678-689页

作者： Bansal, S. Singh, M. Dubey, R. K. Panigrahi, B. K. Indian Inst Technol Delhi Comp Sci & Engn Dept New Delhi 110016 India Robert Bosch Engn & Business Solut Private Ltd He 123 Hosur Rd7th Block Bengaluru 560095 Karnataka India Indian Inst Technol Delhi Elect Engn Dept New Delhi 110016 India

Purpose In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. Methods In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT scans. The model proposed in this study uses a three-step architecture, consisting of a convolutional autoencoder based unsupervised feature extractor, a multi-objective genetic algorithm (MOGA) based feature selector, and a Bagging Ensemble of support vector machines based binary classifier. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID ***). A dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. Results The best performing classifier within 127 ms per image achieved an accuracy of 98.79%, the precision of 98.47%, area under curve of 0.998, and an F1 score of 98.85% on 497 test images. The proposed model outperforms the current state of the art COVID-19 diagnostic techniques in terms of speed and accuracy. Conclusion The experimental results prove the superiority of the proposed methodology in comparison to existing *** study also comprehensively compares various feature selection techniques and highlights the importance of feature selection in medical image data problems.

关键词： Coronavirus (COVID-19) Convolutional autoencoder multi-objective genetic algorithm Feature subset selection

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Optimization of ultra-thin battery heat transfer structures using entropy minimization and multi-objective genetic algorithm

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JOURNAL OF ENERGY STORAGE 2025年 122卷

作者： Yao, Xiaole Li, Yuxuan He, Hao Shi, Qianlei Liu, Qian Zhu, Xiaoqing Ju, Xing Xu, Chao North China Elect Power Univ Beijing Lab New Energy Storage Technol Beijing 102206 Peoples R China

Considering the strict constraints on battery module space and cost, two types of ultra-thin battery heat transfer structures were proposed and numerically optimized in this paper. The grooved patterns of microstructures are developed based on the manifold microchannel (MMC) and manifold micro pin fin (MMPF) concepts. The effects of geometrical parameter variations on the system's thermodynamic irreversibility are investigated. The structural optimization uses the Entropy Generation Minimization method and multi-objective genetic algorithm. The effects of eight independent geometrical parameters on the performance of the micro-structured heat sinks are analyzed by statistical analysis, and the finding is that the inlet manifold width and the height of the microchannel/micro-pin-fin strongly affect the entropy generation of the system. The entropy generation becomes lower as the wider the inlet manifold and the lower the microchannel/micro-pin-fin. The multi-objective genetic algorithm determines that the entropy generation characteristics are a function of these two crucial parameters. The irreversible losses of the optimized MMC and MMPF heat sinks are reduced by 88.53 % and 83.47 %, respectively, and the thermodynamic efficiencies, eta W- S, are increased from 97.51 % and 97.83 % to 99.59 % and 99.75 %, respectively, compared to the initial geometries.

关键词： Battery thermal management micro-structured heat sink Entropy generation minimization multi-objective genetic algorithm

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A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Zhuang, Mengzhen Zhang, Wei Tang, Hongtao Li, Xinyu Wang, Kaipu Wuhan Univ Technol Wuhan Sch Mech & Elect Engn Wuhan 430074 Peoples R China Hubei Prov Engn Res Ctr Robot & Intelligent Mfg Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

关键词： Green shop scheduling multi-objective genetic algorithm Reinforcement learning Machine speed Parameters adjusting strategy

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multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending

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RENEWABLE ENERGY 2024年 225卷

作者： Rawat, Shweta Wagadre, Lokesh Kumar, Sanjay Indian Inst Technol BHU Varanasi Sch Biochem Engn Varanasi 221005 Uttar Pradesh India Bipin Tripathi Kumaon Inst Technol Biochem Engn Dept Dwarahat 263653 Uttaranchal India

Towards establishing low -carbon bio-economy, the energy -rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co -pyrolysis -based valorization of de -oiled microalgae and low -rank coal blend to generate H-2 and CH4 rich syngas. Pyrolysis kinetic models, KissingerAkahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (Ea). The gradual addition of microalgae (0-100%) in coal reduces E-a from 189.11-55.87 kJ/mol and 180.16-54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network -based multi -objective genetic algorithm (ANN-MOGA), respectively. Maximum H-2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 C-degrees) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate -13.8 C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co -pyrolysis approach to gain carbon -neutral energy with zero waste discharge.

关键词： De-oiled microalgae Co-pyrolysis Syngas Artificial neural network multi-objective genetic algorithm Hymethane carrying ratio

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