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multi-objective optimization Design of Buckling-Restrained Braced Frames Based on Performance Evaluation and Risk of Repair Cost and Time

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第3期50卷 1905-1920页

作者： Babaei, Narges Rahgozar, Reza Shojaei, Saeed Shahid Bahonar Univ Civil Engn Dept Kerman Iran

Buckling-restrained braced frames (BRBFs) present a kind of lateral bracing system characterized by their remarkable high-energy dissipation capacity. This study focuses on two BRBFs within 2- and 6-story structures. The frames are meticulously modeled within the OpenSees software. The investigation employs the multi-objective particle swarm optimization (MOPSO) algorithm to ascertain the optimal stiffness modification factor for the braces. This factor is influenced by diverse aspects, including brace length and cross-sectional area-key components in synthesizing the brace structure. The objective of brace optimization lies in minimizing building repair time and cost, necessitating a comprehensive risk assessment. Throughout the optimization procedure, performance evaluation is conducted using the methodology outlined in FEMA P-58. Each optimization stage involves an analysis of the braces utilizing Incremental Dynamic Analysis (IDA) across 22 earthquake records to assess their performance. The optimization outcomes unveil a distinct trend: for a 2-story building, lower values of the stiffness modification factor engender an optimal risk profile concerning repair time and cost. Conversely, a 6-story building tends toward higher values of the stiffness modification factor to achieve an optimal balance between repair time and cost.

关键词： Buckling-restrained braced frames Performance-based design multi-objective optimization Incremental dynamic analysis Risk of repair cost and time

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multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2025年第2期17卷

作者： Ren, Jiongkang Wang, Shisong Ren, Keqi Chengdu Univ Technol Coll Mech & Elect Engn 1 Dongsan Rd Chengdu 610059 Sichuan Peoples R China Chengdu Univ Technol Coll Nucl Technol & Automat Engn Chengdu Sichuan Peoples R China Chengdu Zhongliang Chuangong Technol Co Ltd Chengdu Sichuan Peoples R China

Grinding is a critical method for enhancing the quality of worm tooth surfaces, and its process optimization has long been a significant research focus;however, existing methods are insufficient in addressing the nonlinearity and complexity inherent in the grinding of complex surfaces. In this study, a three-objective optimization function tailored for grinding complex spiral surfaces is developed and experimentally validated. We have successfully applied the innovative integration of the multi-objective Grey Wolf optimization Algorithm (MOGWO) and the optimization function to optimize the grinding process of the Roller Enveloping Worm Reducer (REWR). To account for actual working conditions, we developed constrained models for grinding ratio and machining rigidity and improved the boundary processing method for MOGWO optimization. The enhanced MOGWO demonstrates superior search capabilities during the optimization process, with its optimal solution outperforming traditional optimization algorithms. The optimized grinding process parameters reduce the grinding time by 17.41%, improve the grinding surface quality by 4.46%, and reduce the grinding cost by 1.12% compared with the conventional machining scheme. This provides practical guidance for optimizing the REWR and other complex surface grinding processes.

关键词： Complex grinding process evolutionary algorithm multi-objective optimization multi-objective Grey Wolf optimization Algorithm production cost production rate

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multi-objective optimization of Construction Project Management Based on NSGA-II Algorithm Improvement

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 432-444页

作者： Yang, Yong Men, Jinrui Shandong Huayu Univ Technol Sch Energy & Bldg Engn Dezhou 253000 Peoples R China

the building industry, one of the key components to ensuring a project's successful completion is multi-objective project management. However, due to its own limitations, the traditional multi-objective management approach for projects is no longer able to meet the requirements of building construction and urgently needs to be improved. This is because the construction industry is becoming more competitive and construction standards are improving. Traditional methods for multi-objective optimization typically involve simply summing multiple objectives with weights, overlooking the interdependencies among these objectives. These methods often get trapped in local optimal solutions and rely heavily on predefined models and parameters, limiting their adaptability to sudden changes during the construction process. Therefore, a multi-objective management approach based on multi-objective genetic algorithm for construction projects is proposed. It enables in-depth analysis and comprehensive optimization of the complex relationships between objectives, leading to more informed decisions. By facilitating rapid iteration and adaptation, it enables timely adjustments and optimizations to ensure that project goals remain consistent in complex and dynamic environments. In the experimental validation, the NSGA-II algorithm achieved a significant accuracy of 0.642 and success rate of 0.504 on the VOT dataset, both of which improved by about 1.0% and 0.6% compared to the comparison algorithm. Experimental results on the TrackingNet dataset revealed that the algorithm achieved an accuracy of 0.791 and a success rate of 0.763, while it still maintained an accuracy of 0.542 and a success rate of 0.763 in the face of occlusion. The enhanced multi-objective genetic algorithm had higher accuracy and success rates. This demonstrates the efficiency and excellence of the multi-objective management optimization approach suggested in this study for building projects. The research results hav

关键词： NSGA-II algorithm improvement strategy construction engineering project management multi-objective optimization

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multi-objective optimization of Swirling Impinging Air Jets with Genetic Algorithm and Weighted Sum Method

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Frontiers in Heat and Mass Transfer 2025年第1期23卷 71-94页

作者： Sudipta Debnath Zahir Uddin Ahmed Muhammad Ikhlaq Md.Tanvir Khan Avneet Kaur Kuljeet Singh Grewal Future Urban and Energy Lab for Sustainability(FUEL-S) Faculty of Sustainable Design Engineering(FSDE)University of Prince Edward IslandCharlottetownPE C1A4P3Canada Department of Mechanical Engineering Khulna University of Engineering&Technology(KUET)Khulna9203Bangladesh School of Engineering Newcastle UniversityNewcastle Upon TyneNE17RUUK Dyson Institute of Engineering and Technology Tetbury HillMalmesburySN160RPUK Department of Mechanical and System Engineering Okayama UniversityOkayama7008530Japan Faculty of Sustainable Design Engineering(FSDE) University of Prince Edward IslandCharlottetownPE C1A4P3Canada

Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer *** the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy *** paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling *** governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent *** study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are ***-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling *** aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure *** findings have practical implications for applications requiring efficient *** optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference *** convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet ***,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.

关键词： Jet impingement multi-objective optimization pareto front NSGA-Ⅱ WSM

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multi-objective optimization design for sunspace of traditional dwellings considering energy consumption and thermal comfort in Southern Shaanxi, China

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ENERGY REPORTS 2025年 13卷 4867-4883页

作者： Xu, Juan Yang, Kai Yang, Wenting Lu, Ziliang Liu, Dan Wei, Yajian Changan Univ Architecture Sch Xian Peoples R China Northwest Engn Corp Ltd Xian Peoples R China

Most of the traditional dwellings in southern Shaanxi of China are self-built, and thermal insulation measures are rarely considered in design and construction process. The additional sunspace has the potential inference to improve indoor thermal environment and reduce building energy consumption. This research systematically analyses the designing influence on sunspace about energy consumption through single-element simulation, multi-objective genetic algorithm optimization and dynamic payback period. The results indicate that width and depth of the sunspace, and type of glass are the most influential factors on building heat load, cool load, and thermal comfort duration. Optimized sunspace can increase operating temperature in living room and bedroom by 1.53 degrees C and 1.18 degrees C respectively during winter. At the meantime, the sunspace may further increase indoor operating temperature in summer, but it can be reduced by 0.44 degrees C and 0.78 degrees C respectively through ventilation and shading system. Furthermore, the optimized traditional dwellings reduced their annual energy consumption by 20.72 % with 27.72 % reduction in heating energy consumption during the winter and 0.4 % reduction in cooling energy consumption during the summer. The research findings provide a theoretical basis for the improvement of the indoor thermal environment and energy-saving renovation of traditional dwellings in hotsummer and cold-winter areas.

关键词： Traditional dwellings Additional sunspace multi-objective optimization Energy-saving Thermal environment

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multi-objective optimization of flight schedules to maximize constraint tolerance by local search and archive mechanisms

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ARTIFICIAL LIFE AND ROBOTICS 2025年第2期30卷 289-302页

作者： Ishizuka, Tomoki Murata, Akinori Sato, Hiroyuki Takadama, Keiki Univ Electrocommun Dept Informat Tokyo Japan Elect Nav Res Inst Tokyo Japan Univ Tokyo Informat Technol Ctr Tokyo Japan

To introduce the concept of the "constraint tolerance" (i.e., a feasibility of solutions) in the flight scheduling problem, this paper proposes the optimization method that can find the feasible flight schedules by optimizing the original objective function while maximizing the constraint tolerance as much as possible. The proposed method further is improved by integrating it with the local search and archive mechanisms to obtain a wide range of Pareto-optimal solutions with a high constraint tolerance. A comparison between the proposed method and the conventional methods with or without adding a new objective function to maximize the constraint tolerance shows the statistical superiority of the proposed method.

关键词： multi-objective optimization Evolutionary computation Constraint handling Constraint tolerance Flight schedule

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multi-objective optimization in high-dimensional patent layout using deep Bayesian network and hybrid algorithm

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DISCOVER COMPUTING 2025年第1期28卷 1-22页

作者： Wu, Minghua Jiang, Wenwu Weixian Enterprise Management Consulting Co Ltd Patent Technol Anal Dept Anhui Hefei 230029 Anhui Peoples R China Univ Sci & Technol China Sch Humanities & Social Sci Hefei 230031 Anhui Peoples R China

This study introduces a novel multi-objective optimization framework (DBNO) that integrates deep Bayesian networks with a hybrid algorithm combining random search and innovation diffusion to address high-dimensional patent layout optimization challenges. The framework was developed in response to the increasing complexity of patent layout decisions, where traditional single-objective optimization methods prove inadequate for simultaneously addressing multiple conflicting objectives such as profit, risk, and sustainability. To evaluate the framework's effectiveness, we conducted comprehensive experiments comparing DBNO against established algorithms including genetic algorithm (GA), particle swarm optimization (PSO), and traditional Bayesian optimization methods. Performance metrics encompassed convergence speed, computational efficiency, optimization stability, and solution quality across multiple objectives. The results demonstrate that DBNO consistently outperforms benchmark algorithms, particularly in optimizing sustainability objectives. Notably, DBNO exhibited superior stability and higher success rates in the optimization process compared to GA and PSO, highlighting its robustness in handling complex high-dimensional optimization problems. Furthermore, the integration of innovation diffusion mechanisms significantly enhanced both the efficiency and accuracy of the optimization process. The primary contribution of this research lies in the novel combination of deep Bayesian networks with ensemble random search techniques, resulting in a powerful multi-objective optimization framework. This approach provides an effective solution for high-dimensional patent layout problems while offering new perspectives for patent strategic decision-making. The findings advance the field of multi-objective optimization and establish a foundation for future research in patent portfolio optimization.

关键词： Deep Bayesian network multi-objective optimization Integrated random search Innovation diffusion Patent layout Algorithm comparison Convergence speed Computational efficiency

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multi-objective optimization of a hybrid excitation generator with a parallel magnetic circuit based on the coupling of dual optimization algorithms

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ARCHIVES OF ELECTRICAL ENGINEERING 2025年第1期74卷 127-149页

作者： Sun, Tingjun Hu, Wenjing Meng, Fanxi Wu, Jinke Liu, Yixin Geng, Huihui Liu, Weitao Wang, Wei Shandong Remote Bento New Energy Vehicle Co Tai An 271200 Peoples R China Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China

The coaxial parallel magnetic circuit dual-rotor hybrid excitation structure generator exhibits several advantages, including high output performance, a wide adjustment range, and excellent stability. This study introduces a topology for a parallel magnetic circuit hybrid excitation generator (PMC-HEG) that utilizes a combination of permanent magnet and electrical excitation. It features salient pole rotors and claw pole rotors, with the latter embedded with permanent magnets, sharing a common stator. The analysis of the rotor magnetic field is conducted using both the equivalent magnetic circuit method and the subdomain method. Through an examination of the generator's electromagnetic performance, key rotor parameters related to optimization objectives are identified. Finite element simulation analysis is performed on the rotor parameters, employing various optimization algorithms to enhance the salient pole and claw pole rotors, focusing on the amplitude of the induced electromotive force and the distortion rate of the induced electromotive force as optimization targets. The final optimized parameter values are obtained. A prototype is fabricated and tested, with experimental results confirming the reliability of the optimization method. The optimized parallel magnetic circuit hybrid excitation generator demonstrates an increase in the amplitude of the induced electromotive force, an improvement in the fundamental wave of the induced electromotive force, a reduction in harmonic distortion rate, and a significant enhancement in overall output performance.

关键词： hybrid excitation generator parallel magnetic circuit evolutionary algorithm response surface methodology multi-objective optimization

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multi-objective optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第4期16卷 203-203页

作者： Yang, Rong Lu, Jianxiang Sun, Zhiqi Huang, Wei Guangxi Univ Coll Mech Engn Nanning 530004 Peoples R China

Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a multi-objective Mantis Search Algorithm (MOMSA)-a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based multi-stage Constant Current (SMCC) charging profile-a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 degrees C to 35 degrees C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a multi-objective Particle Swarm optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy's superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency.

关键词： parking charging strategy MOMSA SMCC multi-objective optimization battery degradation

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multi-objective optimization on thermo-structural performance of honeycomb absorbers for concentrated solar power systems

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CASE STUDIES IN THERMAL ENGINEERING 2025年 70卷

作者： Behzad, Masoud Poncet, Sebastien Sarmiento-Laurel, Cristobal Diego Portales Univ Fac Engn & Sci Sch Ind Engn Santiago 8370191 Chile Univ Sherbrooke Dept Mech Engn 2500 Blvd Univ Sherbrooke PQ J1K 2R1 Canada

Honeycomb volumetric solar receivers have emerged as promising candidates for concentrating solar power applications because of their thermal and mechanical properties, enabling the efficient heating of fluids. Despite their potential, challenges remain in optimizing channel design and operating conditions to enhance thermodynamic performance. This study identifies design and operating configurations that maximize the thermodynamic performance and structural reliability of silicon carbide honeycomb volumetric solar receivers, focusing on thermal efficiency and factor of safety. We adopted a multi-objective optimization approach by integrating computational fluid dynamics, heat transfer, and thermal stress analysis. To streamline computational efforts, the Taguchi method was employed, reducing the number of required simulations while maintaining a relative error below 5 %. A critical mass flow to absorbed power ratio of 5 x 10-6 (kg/s)/W was identified, beyond which thermal efficiency stabilizes, providing practical guidance for operational optimization. The optimal configuration achieved a thermal efficiency of 89.3 % and a factor of safety of 87.3 %, with a channel width of 3 mm, a thickness of 0.3 mm, an outlet static pressure of -70 Pa, and a radiation flux of 650 kW/m2. These findings establish a robust framework for optimizing honeycomb receivers, addressing thermal and structural performance while maintaining simplicity in manufacturing processes.

关键词： Honeycomb Volumetric solar receiver Concentrated solar power plant Thermo-structural study Numerical modeling multi-objective optimization

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