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Applicability of genetic algorithm in biochar combustion kinetics with double distributed activation energy model

REACTION KINETICS MECHANISMS AND CATALYSIS 2025年第1期138卷 235-249页

作者： Yang, Yantao Wang, Yunbo Shi, Zhan Li, Yuanna Yang, Mei Lei, Tingzhou Cai, Junmeng Changzhou Univ Inst Urban & Rural Min Changzhou 213164 Jiangsu Peoples R China Shanghai Jiao Tong Univ Biomass Energy Engn Res Ctr Sch Agr & Biol 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

In this study, the genetic algorithm, a stochastic global optimization method, was used to investigate complex reaction kinetics. The genetic algorithm's effectiveness and efficiency were validated through investigating a conventional optimization problem and a theoretically simulated chemical reaction process. The combustion kinetics of biochar derived pinewood sawdust pyrolysis was experimentally investigated, and a distributed activation energy model (DAEM) with a double distribution was utilized to analyze the kinetic behaviors of biochar combustion, and the genetic algorithm was employed to optimize the model parameters. For biochar combustion, two overlapping sub-processes with different activation energy distributions were revealed by the double DAEM: 160-200 kJ mol-1 (peaked at 182.47 kJ mol-1) for the first sub-process and 165-235 kJ mol-1 (peaked at 199.96 kJ mol-1) for the second sub-process. The DAEM with the genetic algorithm for the estimation of model parameters provides a powerful tool for analyzing the thermal decomposition kinetics of complex solid materials.

关键词： Biochar combustion Optimization genetic algorithm Parameter estimation Distributed activation energy model (DAEM)

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Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-objective genetic algorithm

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2025年第1期69卷 159-174页

作者： Sekfali, Nasser Lafifi, Brahim Badji Mokhtar Univ Fac Earth Sci Dept Architecture POB 12 Annaba 23000 Algeria Univ 8 Mai 1945 Fac Sci & Technol Lab Civil Engn & Hydraul POB 401 Guelma 24000 Algeria

Considering uncertainty in the analysis of geotechnical structures is a necessary condition for optimal and robust design. An alternative method for studying the reliability of a mechanically reinforced earth wall in granular soil is used to account for these uncertainties more rigorously. This allows for the inclusion of various uncertainties in a mathematical risk formulation based on random variables. The deterministic model is a benchmark taken from the literature used in a numerical simulation to determine the maximum horizontal displacement of the wall. In this case, the serviceability limit state is considered, allowing the wall's actual behavior to be described. ANOVA was used to identify the most influential parameters on the system's response. As uncorrelated random variables, only the parameters (E, phi and gamma) were considered. The mathematical model serving as the limit state function was numerically predictedby three methods, response surface methodology (RSM), artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), and their predictive capacities were then compared. The results showed that the ANN model outperformed the RSM and ANFIS models regarding prediction. ANN models and multi-objective genetic algorithm (MOGA) are used to optimize the Hasofer-Lind reliability index. The analysis is then carried out by taking into account the various types of functions of parameter distributions, which allowed us to better appreciate the effects of the uncertainties and identify the set of parameters with a high incidence.

关键词： mechanically stabilized earth (MSE) walls numerical modeling reliability analysis RSM ANN ANFIS genetic algorithm

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Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年第2期36卷 1175-1199页

作者： Albus, Marcel Hornek, Timothee Kraus, Werner Huber, Marco F. Fraunhofer Inst Mfg Engn & Automat IPA Nobelstr 12 D-70569 Stuttgart Germany Univ Stuttgart Inst Ind Mfg & Management IFF D-70569 Stuttgart Germany

Assembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies' competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to similar to 2.75x, reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

关键词： Reconfiguration Assembly line balancing genetic algorithm Production

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Optimization of Railway Transportation Planning by Combining TST Model and genetic algorithm

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IEEE ACCESS 2025年 13卷 53266-53275页

作者： Cao, Wei Chen, Fan Beijing City Univ Dept Econ & Management Beijing 101309 Peoples R China Wuhan Railway Vocat Coll Technol Sch Railway Locomot & Rolling Stock Wuhan 430205 Hubei Peoples R China

Railway transportation, a key long - distance freight transport method, faces challenges due to the rapid growth of global logistics demand. These challenges include high transportation costs, low punctuality rates, and inefficient resource utilization. Traditional static optimization methods cannot adapt to dynamic changes and multi-objective optimization requirements. The study proposes an integrated method that combines the Temporal-Spatial Tunnel (TST) model with the genetic algorithm (GA). The TST model describes railway transportation changes dynamically by integrating temporal and spatial dimensions. The GA uses its global search ability to optimize train routing and timetabling. The proposed method enhances the efficiency and flexibility of the railway transportation system. It addresses the issues of low punctuality, inefficient resource utilization, and lack of adaptability to dynamic changes and multi - objective optimization in traditional methods. Experimental results show the superiority of this approach. In urban network scenarios, it achieves a punctuality rate of 94.87%, resource utilization of 89.78%, and a response time of 280.12 seconds. In freight - priority scenarios, the maximum punctuality rate reaches 95.45%. Compared to traditional methods, it significantly improves transportation efficiency and flexibility in multi - objective optimization, offering an effective solution for railway transportation planning under dynamic demands and valuable references for logistics system scheduling optimization.

关键词： Transportation Rail transportation Optimization Dynamic scheduling genetic algorithms Schedules Resource management Costs Adaptation models Logistics Railway transportation planning TST genetic algorithm multi-objective optimization

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Constitutive identification of materials with different symmetry classes through a genetic algorithm

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COMPOSITE STRUCTURES 2025年 360卷

作者： Colatosti, Marco Ongaro, Greta Pingaro, Marco Trovalusci, Patrizia Sapienza Univ Rome Dept Struct & Geotech Engn Via Antonio Gramsci 53 I-00197 Rome Italy

Composite materials play a primary role in many engineering applications. However, their mechanical description proves challenging because, at finer scales, they are characterised by the presence of significant heterogeneities in size and texture, which affect the macroscopic response of the materials. Classical continuum models are not always suitable for describing the macroscopic behaviour of such materials, especially when it is important to consider the microscopic level. To adequately address scale effects, several non-classical/non-local formulations have been proposed in the literature. Among these, the micropolar model, which is a non-local model of "implicit'' type, has proven to be effective in representing the mechanical behaviour of anisotropic media, taking into account the arrangement, size, and orientation of particles. Within this context, this work focuses on modelling composites both as continuous and discrete systems, with the latter providing a finer description of the material. The aim of the study is to identify micropolar elastic constants of composite materials represented as rigid blocks and thin elastic interfaces. A heuristic optimisation approach based on the Differential Evolution algorithm is adopted to derive the constitutive micropolar parameters by exploiting the results of static and dynamic analyses performed on the discrete systems. The obtained results, for different material symmetry classes, indicate that the proposed strategies provide satisfactory outcomes, paving the way for experimental validation and potential engineering applications.

关键词： Composite materials Micropolar continua Identification procedure genetic algorithm Constitutive material properties

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A novel approach in constructing virtual real driving emission trips through genetic algorithm optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Ponce, Jose Barbier, Alvin Palau, Carlos E. Guardiola, Carlos Univ Politecn Valencia Valencia Spain

The Real Driving Emission (RDE) test became a critical part of the process conducted by manufacturers to fulfill the approval procedure of every new vehicle model. This test measures the regulated emissions from a vehicle during a trip, which follows a specific set of operation requirements, aiming to assess the vehicle's emission levels in real-world conditions. Additionally, In-Service Conformity (ISC) tests, which consist in performing an RDE trip, were also introduced to demonstrate vehicles emissions compliance over their lifespan. Considering that modern vehicles embed exhaust emission sensors and connectivity capabilities, it is believed that there is an opportunity for manufacturers to leverage the data generated by these vehicles to forecast the outcomes of an ISC test. However, as this study presents through the analysis of an extensive database of more than 600 trips from a mild-hybrid diesel vehicle, none of the real-world trips might comply with all the driving requirements of the RDE standard. Faced with this outcome, this work proposes the application of a genetic algorithm (GA) optimization to construct virtual RDE trips from real-driving data. In particular, the proposed methodology leverages such algorithm to combine real driving fragments from various trips in order to align with the main RDE trip requirements. The methodology focuses on vehicle, engine, and exhaust after-treatment variables, utilizing signal optimization connections to create a realistic analysis of vehicle pollutants. The research suggests that a combination of vehicle speed, coolant temperature, exhaust temperature, and Selective Catalytic Reduction (SCR) load leads to a significant number of RDE-compliant results under simplified legislative conditions, from which emissions profiles could be assessed. The proposed methodology details the development of an Adaptive genetic algorithm (AGA) and the data pipeline to create specific RDE trips, offering the capability to customize t

关键词： genetic algorithm Real-driving emissions Driving cycles Optimization Automotive monitoring In-service conformity

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Real-Time Optimization Improved Model Predictive Control Trajectory Tracking for a Surface and Underwater Joint Observation System Based on genetic algorithm-Fuzzy Control

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REMOTE SENSING 2025年第5期17卷

作者： Wu, Qichao Nie, Yunli Wang, Shengli Zhang, Shihao Wang, Tianze Huang, Yizhe Shandong Univ Sci & Technol Coll Ocean Sci & Engn Qingdao 266590 Peoples R China

Aiming at the high-precision trajectory tracking problem of the new surface and underwater joint observation system (SUJOS) in the ocean remote sensing monitoring mission under complex sea conditions, especially at the problem of excessive tracking errors and slow convergence of actual trajectory oscillations caused by the wide range of angular changes in the motion trajectory, a real-time optimization improved model predictive control (IMPC) trajectory tracking method based on fuzzy control is proposed. Initially, the novel observation platform has been designed, and its mathematical model has been systematically established. In addition, this study optimizes the MPC trajectory tracking framework by integrating the least squares adaptive algorithm and the Extended Alternating Direction Method of Multipliers (EADMM). In addition, a fuzzy controller, optimized using a genetic algorithm, an output of real-time optimization coefficients, is employed to dynamically adjust and optimize the bias matrix within the objective function of the IMPC. Consequently, the real-time performance and accuracy of the system's trajectory tracking are significantly enhanced. Ultimately, through comprehensive simulation and practical experimental verification, it is demonstrated that the real-time optimization IMPC algorithm exhibits commendable real-time and optimization performance, which markedly enhances the accuracy for trajectory tracking, and further validates the stability of the controller.

关键词： remote sensing monitoring trajectory tracking IMPC fuzzy control genetic algorithm real-time optimization

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Scheduling two-stage healthcare appointment systems via a knowledge-based biased random-key genetic algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Yang, Fajun Li, Chao Wang, Feng Yang, Zhi Gao, Kaizhou Shanghai Univ Sch Management Shanghai 200444 Peoples R China Shanghai Int Studies Univ Xianda Coll Econ & Humanities Sch Business Shanghai 200083 Peoples R China Hunan Univ Business Sch Changsha 410082 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

To address the scheduling problem of two-stage healthcare appointment systems, previous studies always assume that a positive linear correlation is obeyed between the customer waiting time and service dissatisfaction, and an arrived customer is served immediately if the provider at the first stage becomes available, which usually leads to heavy congestion at the second stage and a rapid decline in service satisfaction. To tackle this problem further, this paper assumes that customer waiting time within different ranges impacts service dissatisfaction differently. Then, it develops an efficient real-time scheduling strategy to decide the exact starting time of each customer's service at the first stage. Considering no-shows and non-punctual appointments, a knowledge-based biased random-key genetic algorithm (K-BRKGA) is used to determine the number of customers at each appointment slot, such that the total weighted cost associated with customers' waiting time, providers' idle time, and overtime at two stages can be minimized. Based on the data sets used, K-BRKGA reduces the total cost by 2.01 % and 1.01 % compared to the other two famous algorithms.

关键词： Healthcare appointment system genetic algorithm No-show Unpunctuality Scheduling

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Prestress state, parametric analysis study and genetic algorithm-based tentative design of a novel pentagonal cable dome structure with tri-strut layout

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THIN-WALLED STRUCTURES 2025年 208卷

作者： Lv, Hui Yi, Lu Zhu, Zhongyi Dong, Shilin Shao, Lian Wu, Xiaoshun Nanchang Hangkong Univ Coll Civil Engn & Architecture Nanchang 330063 Peoples R China Zhejiang Univ Space Struct Res Ctr Hangzhou 310058 Peoples R China Beijing Inst Architectural Design Beijing 100045 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Space Struct Hangzhou 310058 Peoples R China Jiangxi Univ Sci & Technol Sch Software Engn Ganzhou 330013 Peoples R China

To improve the structural performance and economy of the large-span roof structure from the structural system level, a novel structural form of pentagonal cable dome with tri-strut layout and large central opening is proposed, which can be applied to the large-span stadium ring canopy structure. Different from Fuller's traditional conception of the tensioning whole, this type of system has three struts intersecting at the same chord node, which reduces the amount of ring and diagonal cables, facilitates tensioning construction, and improves overall stability. For this large-opening cable dome, a general formula for calculating the internal force of prestressed state rods was derived based on the nodal equilibrium equations, and the effects of several parameters on the distribution of pre-tension in the cable dome were analyzed and studied to understand the distribution pattern and characteristics of the cable dome pre-stress. A large-opening stadium canopy with a span of 100 m is taken as an example. The parameter sensitivity and correlation analysis of the structural performance reveals that the main influence of the economy of this cable dome is the pre-tension level. In contrast, the geometric parameter produces a much lower effect on the structural economy. The optimal design and trade-off analysis of this cable dome structure based on two genetic algorithms show that appropriately increasing the structural vector height and decreasing the thickness while satisfying the stability improves the structural stiffness and economy. The analysis results show that this new type of cable dome has superior technical and economic indicators, and the research in this paper provides a new form and new ideas for the analysis and design, optimization research, and modeling of the cable dome.

关键词： Pentagonal cable dome with tri-strut layout and large central opening Structural morphology Simplified prestress calculation method Parameter sensitivity analysis Optimal design genetic algorithm Technical and economic performance

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Application of entertainment e-learning mode based on genetic algorithm and facial emotion recognition in environmental art and design courses

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Li, Shanshan Zhengzhou Coll Finance & Econ Zhengzhou 450000 Peoples R China

This article is based on facial recognition and further designs an entertainment electronic learning mode. Facial emotion recognition may be affected by light, angle, and expression. In order to improve recognition accuracy and stability, distortion adjustment techniques were used to process facial images, ensuring that the model can accurately capture and recognize the features of facial expressions. The study applies online facial emotion recognition to entertainment electronic learning modes, where learners interact with the system. The system can detect and recognize learners' facial expressions in real-time, and provide corresponding feedback and learning resources based on different expressions. By collecting and analyzing experimental data, evaluate the practicality of the model and the level of acceptance and satisfaction of learners towards the model. The entertainment electronic learning mode based on facial recognition provides an innovative learning approach by constructing a pattern architecture, distortion adjustment, and applying online facial emotion recognition. By optimizing the positioning and integrating the entertainment electronic learning mode with environmental art and design courses, we aim to enhance students' learning motivation and interest. Develop optimization strategies to enhance students' comprehensive abilities in the field of environmental art and design.

关键词： genetic algorithm Facial emotion recognition Entertainment e -learning mode Environmental art design Course application

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