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The tiered competition genetic algorithm and its application in the wind-resistant optimization of a cable-stayed mast structure

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ENGINEERING STRUCTURES 2025年 327卷

作者： Xu, An Han, Liang Fu, Jiyang Huangfu, Shangqian Zhao, Ruohong Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou Peoples R China

The wind-resistant optimization design of a cable-stayed mast structure involves constraints related to component stress, stability, as well as constraints on the top displacement and interstory drifts of the main mast structure. Due to the difficulty of explicitly expressing the constraints using design variables, deterministic algorithms such as the optimal criterion method are not feasible for optimizing the structure. This paper proposes a Tiered-Competition genetic algorithm (TCGA), which improves the conventional GA in multiple aspects including constraint handling, tiered competition mechanism, individual selection strategy, and dynamic crossover and mutation. The superiority of the proposed method is validated using three benchmark functions and a 72-bar spatial truss benchmark structure. Finally, the proposed TCGA method is applied to the structural optimization of a 288 m tall cable-stayed mast structure. The results demonstrate that the TCGA method effectively addresses optimization problems for such complex structures with multiple types of constraints. The optimization process converges rapidly and steadily, and all constraints are satisfied after structural optimization.

关键词： Lattice structure Cable-stayed mast structure Wind effect Structural optimization genetic algorithm

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

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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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Modelling and optimization of microgrid with combined genetic algorithm and model predictive control of PV/Wind/FC/battery energy systems

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ENERGY REPORTS 2025年 13卷 238-255页

作者： Agoundedemba, Maklewa Kim, Chang Ki Kim, Hyun-Goo Nyenge, Raphael Musila, Nicholas Kenyatta Univ Dept Phys POB 43844-00100 Nairobi Kenya Korea Inst Energy Res Renewable Energy Big Data Lab 152 Gajeong Ro Daejeon 34129 South Korea Univ Sci & Technol Energy Engn Daejeon 34113 South Korea Korea Inst Energy Res Renewable Energy Inst Daejeon 34129 South Korea

Microgrid systems with hybrid renewable energy resources, such as PV, wind, have been widely used with storage devices to supply power to certain load demands. However, technical issues and fewer benefits can occur due to their intermittent nature and the high investment costs associated. So, an accurate model, sizing, and management approach are required to maximize the operational benefits of the microgrid with battery energy storage systems and fuel cells. This study used the combined genetic algorithm (GA) and model predictive control (MPC) to size and optimize the hybrid renewable energy PV/Wind/FC/Battery subject to certain constraints on the power flow and battery state of charge. The data used to validate the model of the system was from the University of California San Diago of 13.5 GWh a year. The main objective was to minimize the cost of energy (COE), power supply probability (LPSP) and the net present cost, by GA. Another goal was to minimize the cost of power imported from the main grid over the time horizon. This was done using MPC based on forecasted data. The results showed a total energy generation of 17.29 GWh in a year. A microgrid produced a cheap cost of energy of $0.19/kWh. A LPSP was 0% indicating that technically the system is viable. The optimized power flow maintained the battery's state of charge within the safe range of 20-95 %, significantly enhancing battery longevity by reducing degradation from frequent charging cycles. The total proposed system relies on the main grid only 5.80 % compared to the current real installed where 15% relies on the main grid. Additionally, the proposed system resulted in a carbon dioxide reduction of 4412.108 tCO2 annually, demonstrating the environmental benefits of the optimized microgrid.

关键词： Microgrid system Net present cost Cost of energy PV/Wind/FC/Battery genetic algorithm Model predictive control

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Optimal Requirements of Spare Transformers and Mobile Units for Distribution Substations via genetic algorithm and Monte Carlo Techniques

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IEEE TRANSACTIONS ON POWER DELIVERY 2025年第1期40卷 261-272页

作者： Mendes, Venus Liria Silva da Silva, Armando Martins Leite Costa, Joao Guilherme de Carvalho Hamoud, Gomaa A. Pontif Catholic Univ Rio Janeiro PUC Rio Dept Elect Engn BR-22451900 Rio De Janeiro Brazil Univ Fed Itajuba Inst Elect Syst & Energy BR-37500903 Itajuba Brazil Hydro One Ltd Toronto ON Canada

This paper proposes a new optimization method based on enhanced genetic algorithm (GA) and Monte Carlo simulation (MCS) techniques, which are simultaneously applied to size regular spare transformer (RST) and mobile unit substations (MUS) stocks for distribution substations. The aim is to serve a group of electrical energy distribution substations to mitigate possible losses caused by load curtailments due to major failures that affect the substation transformers. The proposed method includes the use of resources such as MUS and load transfer, in addition to representing the expansion of the transformers group in operation and the increase in power demand, over a specified planning horizon, considering all waiting times inherent to system actions, e.g.,: RST installation, MUS connection, stock replenishment, etc. Two real systems with different characteristics are used to illustrate the proposed method, allowing the analysis of results obtained from different scenarios and parameters.

关键词： Substations Power transformers Costs Reliability Monte Carlo methods genetic algorithms Power system reliability Biological system modeling Load modeling Markov processes Distribution substations genetic algorithm mobile unit substations Monte Carlo simulation reliability assessment spare transformer stock sizing

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An Effective genetic algorithm for Mixed Precision

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IEEE ACCESS 2025年 13卷 62739-62752页

作者： Zhang, Wanyu Shang, Yu Tsao, Min Li, Yiwei Song, Xiaoyu Portland State Univ Dept Elect & Comp Engn Portland OR 97207 USA Siemens EDA Fremont CA 94538 USA

The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic algorithm to determine the maximum number of variables that can sustain precision alterations without compromising the desired error threshold. The proposed approach is evaluated across various benchmark programs, analyzing the effects of precision tuning under increasing error thresholds. Our findings reveal that, for a majority of these programs, reducing precision through partitioning leads to significant performance enhancements, with improvements of up to 15%.

关键词： Tuning Taylor series Accuracy Error analysis Partitioning algorithms Computational modeling Computational efficiency Approximation algorithms Heuristic algorithms Optimization Floating-point arithmetic algorithmic differentiation dynamic program analysis mixed precision genetic algorithm

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Enhanced single-diode model parameter extraction method for photovoltaic cells and modules based on integrating genetic algorithm, particle swarm optimization, and comparative objective functions

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2025年第2期24卷 1-18页

作者： Abdulrazzaq, Ali Kareem Bognar, Gyorgy Plesz, Balazs Univ Thi Qar Dept Elect & Elect Engn Nassiriya 64001 Iraq Tech Univ Chemnitz Professorship Circuit Design D-09126 Chemnitz Germany Budapest Univ Technol & Econ Dept Electron Devices Hungarian Scientists Tour 2 H-1117 Budapest Hungary

Accurate modeling of the operational behavior of photovoltaic systems is crucial to optimizing and predicting system performance. One of the well-established and widely used modeling techniques is the single-diode equivalent circuit that delivers a sufficiently accurate description of the electric behavior of both photovoltaic cells and modules under various operational conditions. The single-diode model uses five parameters to reproduce the I-V curve for specific operational conditions. However, these five parameters must be extracted from measured or simulated I-V curves. This paper proposes a novel, accurate, and fast method for extracting the single-diode model's five parameters from measured I-V curves based on a genetic algorithm combined with particle swarm optimization to find the optimal controlling parameters of the genetic algorithm. This approach results in a significant performance improvement in accuracy and convergence speed. The paper also proposes a concept for determining the optimum number of current-voltage data points in the I-V curve, enabling an optimum trade-off between a sufficiently high accuracy and computational costs. Finally, the effect of different objective function formulations on the result has been investigated by comparing the usage of three different objective functions: the implicit form of the single-diode model, the Lambert W-function-based formulation of the explicit single-diode model, and a system of equations based on least square fitting. From the results, it could be concluded that the implicit formulation of the single-diode model delivered the best results compared to the two other formulations. Performance evaluations showed significantly lower error values than recent literature, with mean percent errors of 0.038%, 0.34%, and 0.87% received for the investigated monocrystalline cell, poly-crystalline module, and amorphous module, respectively. The computational cost was reduced by more than 60% after determining the o

关键词： I-VCharacteristics genetic algorithm Single-diode model Solar cell Parameters extraction

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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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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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Study on modular design methodology of marine SMR system based on fuzzy hierarchical clustering and improved genetic algorithm

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PROGRESS IN NUCLEAR ENERGY 2025年 185卷

作者： Tang, Zhihong Wang, Liyuan Guo, Shusheng Liang, Guopeng Zhang, Wenjun Zhang, Lide Rui, Min Guan, Guan Wang, Yunlong China Nucl Power Technol Res Inst Co Ltd Shenzhen 5128026 Peoples R China Dalian Univ Technol Sch Naval Architecture Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Marine Small Modular Reactors (MSMR) integrate SMR technology with ship technology, offering unique value in meeting the energy demands of the open ocean and remote islands. However, the design and construction of MSMR face challenges such as space constraints, complex system integration, and the need to adapt to advanced ship modular construction technologies. Therefore, efficient modular partitioning methods are required to enhance overall efficiency and reliability. The module partitioning of MSMR systems takes into account multiple factors and is a combinatorial optimization problem with performance constraints. This study aims to reflect the internal structure of the system hierarchical tree, provide clear guidance for module partitioning, and improve the computational efficiency of solving combinatorial problems. This paper propose a module division and optimization method for MSMR systems based on fuzzy hierarchical clustering and a genetic algorithm. Initially, the components of the small modular reactor power plant system are clustered into modules of different levels using fuzzy hierarchical clustering. Subsequently, a genetic algorithm is employed to solve the combinatorial optimization problem of the module division scheme, resulting in the optimal division scheme. The feasibility and effectiveness of the method are verified through the modular case of the Radioactive Waste Gas System (WGS). This method can provide guidance for the modularization design of the entire ocean modular reactor system. The method provided in this article can provide a research foundation for future modular design of MSMR and improve design efficiency.

关键词： MSMR Modular design Hierarchical clustering genetic algorithm

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Comparing the performance of genetic algorithm and particle swarm optimization algorithm in allocating and scheduling fire stations

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2025年第1期22卷 445-458页

作者： Kheirdast, A. Jozi, S. A. Rezaian, S. Tehrani, M. M. E. Islamic Azad Univ Fac Marine Sci & Technol Dept Environm Management North Tehran Branch Tehran Iran Islamic Azad Univ Dept Environm North Tehran Branch Tehran Iran Islamic Azad Univ Dept Environm Shahrood Branch Shahrood Iran Islamic Azad Univ Dept Environm North Tehran Banch Tehran Iran

genetic algorithm (GA) and Particle Swarm Optimization algorithm (PSOA) have positive effects on the allocation and scheduling of the stations, this research seeks to find which one of these two methods is more appropriate to shorten the time to reach fire/incident site in the Region 19 of Tehran. This is an applied type of research. Data analysis was carried out using NFPA standards and MATLAB software. The statistical population includes 8 fire stations and 250 personnel of the stations, and sampling volume was obtained using Morgan's table (n = 148). In order to efficiently assign and schedule fire stations to arrive at the site, a linear numerical programming model was presented with the aim of minimizing the arrival time and taking into account the effect of firemen's fatigue (alpha = 0.1). Findings of the research showed that the operation processing time (of fire extinguishing) had a normal distribution with a mean of 40 min and a variance of 10 min, independent of the severity of the incident. Also, fatigue coefficient was calculated 0.1 by analyzing the sensitivity of the solution time of the algorithm with changes [0-1]. Initial standard travel time, with an average speed of 47 km/h and a density factor of 1.24, was 5min:20s. Solving the problem in large and small dimensions showed that the initial power effect of each fire station is 0.36 according to the fatigue level of the forces. Based on the obtained results, GA performs better in terms of problem solution time, and the improved PSOA also has higher quality answers.

关键词： genetic algorithm Particle swarm optimization algorithm Firefighting Fire Incident The region 19 of Tehran city

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