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Comprehensive optimization and Design of an Electric Vehicle Battery Box Side Profile for Lightweight and Crashworthiness Using a Novel Hybrid Structure

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2037-2037页

作者： Arslan, Muhammet Karamangil, Mehmet Ihsan Jaguar Land Rover Banbury Rd Gaydon CV35 0RR England Bursa Uludag Univ Fac Engn Dept Automot Engn TR-16059 Bursa Turkiye

Lightweighting is a critical focus in the transportation sector, directly enhancing efficiency and significantly reducing costs. In electric vehicle (EV) design, the body surrounding the battery must effectively absorb impact, especially during crashes. This study aims to improve the crash performance of the side profiles in the battery box of an M1 category vehicle. It is based on the crash test in Annex 8D of the ECE R100 regulation. In this study, the safe displacement at which the battery will not deform is set as 20 mm, and the maximum force and energy absorption at this displacement are compared. In total, 33 different electric and hybrid vehicle models were benchmarked in this study. L-shaped geometry and aluminum materials are generally preferred;this study focuses on using glass-fiber-reinforced polymer (GFRP) pultruded profiles to make batteries more durable and lighter. The GF800 material was selected for its superior mechanical strength among glass fiber composites. A virtual tensile test verified its properties. A unique hybrid model combining honeycomb and auxetic geometries was developed, showing a crash performance improvement of similar to 360% over honeycomb structures and similar to 88% over auxetic structures. Through multi-objective optimization using artificial neural networks (ANNs), 27 models were analyzed, leading to an optimized design. The final design resulted in the battery box side profile being 23.9% lighter and 38.6% cheaper, and exhibiting a performance 3% higher. This study demonstrates significant advancements in EV safety and cost efficiency, highlighting the practical benefits of innovative material and design approaches.

关键词： hybrid structure multi-objective optimization glass-fiber-reinforced polymer (GFRP) battery box crashworthiness pultrusion

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Joint trajectory and beamforming optimization for UAV secure communications with passive coupled phase-shift/active STAR-RIS

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DIGITAL SIGNAL PROCESSING 2025年 156卷

作者： Zhang, Qin Zhao, Yang Li, Hai Song, Zhengyu Hou, Shujuan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Recently, the concept of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been proposed to achieve a full-space (i.e., 360(degrees)) reconfigurable wireless environment. By exploiting the capability of manipulating signal propagation in both transmission and reflection spaces, the STAR-RIS possesses great potential to boost the security in unmanned aerial vehicle (UAV) communications. In this paper, the trajectory and beamforming optimization are investigated for secure transmission in STAR-RIS-assisted UAV communication systems. Unlike existing studies on the STAR-RIS that mostly focus on the passive ideal model, we consider the more practical passive coupled phase-shift model and the active model of the STAR-RIS. Under the multi-user multi-eavesdropper scenario, we aim to maximize the minimum average secrecy rate by jointly optimizing the UAV-base station's (UAV-BS's) beamforming, UAV's trajectory, and the STAR-RIS's transmission and reflection (T&R) coefficients. To tackle the non-convex optimization problem, the original problem is decomposed into three subproblems, and efficient alternating algorithms are proposed by leveraging the semi-definite relaxation, the successive convex approximation, and penalty-based approaches. Simulation results show that: 1) the practical coupled phase-shift model of passive STAR-RIS suffers a security performance degradation compared with the ideal independent phase-shift model, but it still outperforms the conventional RIS;2) the active STAR-RIS with sufficient power budget outperforms its passive counterpart, while behaves slightly worse under unreasonable power budget;3) the optimization of T&R coefficients drives the UAV's trajectory closer to the STAR-RIS, and the amplitudes of T&R coefficients highly depend on the UAV's trajectory in order to ensure the secrecy rate fairness.

关键词： Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) Unmanned aerial vehicle (UAV) Secure communication Trajectory Beamforming Coupled phase shifts

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optimization of fatigue life of the seismic vibrator baseplate considering the coupling effect of welding residual stress

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ALEXANDRIA ENGINEERING JOURNAL 2025年 112卷 551-568页

作者： Chen, Zhen Wang, Qiaomu Nie, Shulong Jing, Shuang Kong, Bo Luan, Nan Southwest Petr Univ Dept Mechatron Engn Chengdu 610500 Sichuan Peoples R China Oil & Gas Equipment Technol Sharing & Serv Platfor Chengdu 610500 Sichuan Peoples R China SINOPEC Key Lab Seism Elast Wave Technol Beijing Peoples R China Southwest Oil & Gas Field Branch China Natl Petr Corp Beijing Peoples R China Baker Hughes Malaysia 25th floorMenara Tan & Tan 207Jalan Tun Razak Kuala Lumpur 50400 Malaysia

The seismic vibrator baseplate (SVB) is a welded structure. Ignoring welding residual stress (WRS) and only considering cyclic stress can decrease the accuracy of fatigue life assessments. This study aims to investigate the fatigue life prediction and optimization of SVB under the influence of WRS. A welding model of SVB and an SVBground excitation model were developed, and the stress distribution of SVB was determined using the finite element method. A fatigue life prediction study that incorporates WRS, based on a modified S-N curve, was conducted. A multi-objective fatigue life optimization analysis was performed using genetic algorithms. The results shows that the backing weld (BW) of the SVB is the most susceptible area to fatigue failure. Under the combined effects of WRS and working stress, the fatigue life of the SVB is 8.14 years. When considering WRS, the prediction results align well with field data, showing an error margin of less than 5%. WRS is identified as a critical factor influencing the fatigue life of SVB, and optimizing welding parameters can effectively reduce WRS, thus extending the service life. The optimal welding parameters were found to be welding interpass temperature of 105 degrees C and welding speed of 10.23 mm/s. After optimization, the fatigue life of the SVB increased to 10.23 years, a 25.68 % improvement over the pre-optimization condition. Previous studies did not consider the influence of WRS, whereas this work integrates it into the fatigue analysis, producing a modified S-N curve that enhances the accuracy of life prediction. Furthermore, multi-objective optimization algorithms and fusion decision-making methods were applied to the fatigue life optimization of the SVB, offering novel research approaches and methodologies. This study provides theoretical guidance for the structural design and optimization of the SVB, aligning with practical engineering needs and showing substantial application value.

关键词： Seismic vibrator baseplate Welding residual stresses Fatigue life prediction and optimization Finite element method Modified S -N curve

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Constraint-aware and multi-objective optimization for micro-service composition in mobile edge computing

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SOFTWARE-PRACTICE & EXPERIENCE 2024年第9期54卷 1596-1620页

作者： Wu, Jintao Zhang, Jingyi Zhang, Yiwen Wen, Yiping Nanjing Univ Informat Sci & Technol Sch Software Nanjing Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China Hunan Univ Sci & Technol Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan Hunan Peoples R China

As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro-services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro-services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro-services constructed by ignoring the processing capabilities of different micro-services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro-services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro-service composition. In order to improve edge resource utilization and user experience on micro-service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro-services composition problem in MEC as a constrained multi-objective optimization problem. Then, a micro-service composition optimization method M3C combining graph search and branch-and-bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

关键词： micro-service composition micro-services mobile edge computing multi-objective optimization

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MOTEA-II: A Collaborative multiobjective Transformation-Based Evolutionary Algorithm for Bilevel optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第2期29卷 474-489页

作者： Chen, Lei Cheung, Yiu-Ming Liu, Hai-Lin Lai, Yutao Guangdong Univ Technol Sch Math & Stat Guangzhou 510006 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

Evolutionary algorithms (EAs) for optimization have received wide attention due to their robustness and practicality. However, the traditional way of asynchronously handling bilevel optimization problems (BLOPs) ignores the benefits brought by effective upper- and lower-level collaboration. To address this issue, this article proposes a collaborative multiobjective transformation (MOT)-based EA (MOTEA-II). In MOTEA-II, the BLOP is handled within a decomposition-based multiobjective optimization paradigm using a two-stage collaborative MOT strategy. The stage-1 MOT focuses on multiple lower-level optimizations and collaboration, while stage-2 collaborates the upper-level optimization with lower-level optimization, which makes simultaneously horizontal and vertical optimization information sharing in bilevel optimization possible. In addition, a dynamic decomposition strategy is further proposed to reconstruct the hierarchy relationship in collaborative multiobjective optimization, facilitating the adaptive and flexible importance control of the upper-level objective optimization and lower-level optimality satisfaction for better-bilevel search efficiency. Empirical studies are conducted on two groups of commonly used BLOP benchmark suites and four practical applications. Experimental results show that the proposed collaborative MOTEA-II can achieve performance comparable to that of the previous MOTEA and three other representative EA-based bilevel optimization approaches, but using much fewer computational resources.

关键词： optimization Collaboration Search problems Evolutionary computation Metaheuristics Vectors multitasking Linear programming Computational efficiency Benchmark testing Collaborative multiobjective transformation dynamic decomposition evolutionary bilevel optimization

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A Prediction-Based multi-objective VM Consolidation Approach for Cloud Data Centers

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Computers, Materials & Continua 2024年第7期80卷 1601-1631页

作者： Xialin Liu Junsheng Wu Lijun Chen Jiyuan Hu School of Computer Science Northwestern Polytechnical UniversityXi’an710005China School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710021China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi’an710021China School of Software and Microelectronics Northwestern Polytechnical UniversityXi’an710005China School of Automation Xi’an University of Posts and TelecommunicationsXi’an710021China

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource *** paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical *** use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)*** results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

关键词： VM consolidation prediction multi-objective optimization machine learning

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multi-objective Robust optimization and Decision-Making Using Evolutionary Algorithms 23

Multi-objective Robust Optimization and Decision-Making Usin...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Yadav, Deepanshu Ramu, Palaniappan Deb, Kalyanmoy Indian Inst Technol Madras Chennai Tamil Nadu India Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798400701191

Evolutionary multi-objective optimization (EMO) algorithms are predominantly used for solving multi- and many-objective optimization problems to arrive at the respective Pareto front. From a practical point of view, it is desirable for a decision-maker (DM) to consider objective vectors that are less sensitive to the small perturbation in design variables and problem parameters. Such insensitive, yet closer to Pareto-optimal solutions, lie on the so-called robust front. In real-world applications, such as engineering design and process optimization problems, perturbations in variables come from manufacturing tolerances, uncertainties in material properties, variations in operating conditions, etc. The existing EMO literature on robustness studies emphasized on finding the entire robust front, but hardly considered robustness in both optimization and decision-making tasks. In this paper, we propose and evaluate different algorithmic implementations of three aspects - multi-objective optimization, robustness consideration, and multi-criterion decision-making - together. Based on experimental results on two to eight-objective problems, we discuss the outcomes and advantages of different integration approaches of these three aspects and present the most effective combined approach. The results are interesting and should pave the way to develop more efficient multi-objective robust optimization and decision-making (MORODM) procedures for handling practical problems with uncertainties.

关键词： Evolutionary Algorithms Pareto Front Robust Front multi-criteria Decision-making R-NSGA-III

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multi-objective optimization of school environments to foster nature connectedness using NSGA-III in school design

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Asian Journal of Civil Engineering 2025年第2期26卷 515-530页

作者： Walimbe, Sonali Nandineni, Rama Devi Rege, Sumita Manipal School of Architecture & amp Planning Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Occupational Therapy Manipal College of Health Professions Manipal Academy of Higher Education Karnataka Manipal 576104 India

The integration of nature into school environments has been shown to enhance student well-being and academic performance, fostering a deeper sense of connection to the natural world. However, designing school infrastructure that balances nature exposure, sustainability, and cost-effectiveness remains a challenge. This study addresses the multi-objective optimization of school construction designs to foster nature connectedness using the non-dominated sorting genetic algorithm III (NSGA-III). The optimization objectives include maximizing green space for nature exposure, minimizing construction and maintenance costs, maximizing sustainability in materials and processes, and optimizing space utilization efficiency. Constraints related to budget, space, and environmental regulations are also incorporated. By applying NSGA-III, this research generates Pareto-optimal solutions that offer trade-offs between competing objectives, such as enhancing nature exposure while controlling costs and ensuring sustainability. The study compares these optimized designs with traditional school construction approaches, highlighting the benefits of using multi-objective optimization in creating environmentally conscious, cost-effective educational spaces. The results demonstrate that NSGA-III is an effective tool for optimizing school designs that prioritize nature connectedness while adhering to practical constraints. This research provides valuable insights for construction managers, architects, planners, and policymakers involved in the design and construction of sustainable educational environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cost-efficiency Green spaces multi-objective optimization Nature connectedness NSGA-III School construction Sustainable architecture

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multi-objective optimization for ultrasound-assisted extraction of phytochemicals from Moroccan Retama raetam using a solvent system: Comparison of mixture design methodology and artificial neural network modeling

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SEPARATION SCIENCE AND TECHNOLOGY 2023年第8期58卷 1433-1449页

作者： El Baakili, Aafaf Fadil, Mouhcine Es-Safi, Nour Eddine Mohammed V Univ Rabat Mat Sci Ctr MSC LPCMIO Rabat Morocco Mohammed V Univ Rabat Mat Sci Ctr MSC LPCMIO Ecole Normale Super Ave Mohamed Bel Hassan OuazzaniPOB 5118 Takaddoum Rabat Morocco

This study was designed to predict the optimized solvent mixture for extracting phenolic compounds from Moroccan Retama raetam. A mixture design methodology (MDM) and artificial neural networks (ANNs) were employed in a solvent system containing water, ethanol, and methanol to optimize the three responses: extraction yield, total phenol content (TPC), and antioxidant activity by 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay. After the statistical analysis of the MDM results, the responses were separately and simultaneously optimized according to the validated special cubic models. The results obtained through MDM showed that the optimal solvent system was a water:methanol (41:59 v/v) binary mixture and the simultaneous optimal responses were 30.29%, 250 mg GAE/g, and 79.8 mu g/ml for extraction yield, total phenolic content, and DPPHIC50, respectively. The mixture design matrix was submitted to the ANNs approach based on a multi-layer consisting of five neurons in a single hidden layer and three input components associated with three output responses. The results obtained through ANN were close to those obtained through MDM with an optimal binary mixture of water:methanol (38:62 v/v) and optimal responses of 28.97%, 256.42 mg GAE/g, and 83.07 mu g/ml for extraction yield, total phenolic content, and DPPHIC50, respectively. The comparison of the two optimization approaches showed that both MDM and ANNs could accurately predict the studied responses. However, ANNs models' parameters were slightly higher than MDM ones. Our findings can provide a practical way for using both ANNs and MDM approaches in optimizing the bio-industry process and studying the bioactivity of selected compounds.

关键词： Retama raetam phenolic compounds antioxidant activity mixture design methodology Anns modeling

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Streamlining extracellular polymeric substance removal: Fuzzy multi-objective optimization of ultrasonic-Fenton treatment

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RESOURCES ENVIRONMENT AND SUSTAINABILITY 2024年 15卷

作者： Choi, Angelo Earvin Sy Evangelista, Danielle Grace Ortenero, Joseph R. De La Salle Univ Dept Chem Engn 2401 Taft Ave Malate 0922 Manila Philippines

Managing high water content sludge in wastewater treatment is crucial for sustainability. This involves a complex challenge of maximizing the removal of loosely bound extracellular polymeric substances (LB-EPS) while minimizing ultrasonic-Fenton process costs. This study introduces a novel approach to address these conflicting objectives by adopting fuzzy multi-objective optimization. This method reconciles the conflicting objectives by identifying the optimal conditions for ferrous ion (Fe2+) dosage, hydrogen peroxide (H2O2) dosage, and ultrasonication time. The optimization model incorporates empirical equations that define the effects of Fenton's reagent and ultrasonication on LB-EPS removal, as well as considerations for material and electricity usage costs and the cumulative uncertainties associated with experimental runs. To effectively capture the trade-offs between EPS removal and process costs, the epsilon -constraint method was utilized to delineate the Pareto front. This approach significantly enhances LB-EPS removal from anaerobically digested sludge and establishes boundary limits within the Pareto front for practical application within the context of fuzzy optimization. The optimized solution derived from this innovative approach resulted in the conditions of 10 mM Fe2+ dosage, 100 mM H2O2 dosage, and 10 min of ultrasonication. This configuration achieves an impressive 60.7% +/- 3.7% LB-EPS removal while maintaining a cost of 26.6 USD/L and ensuring 100% overall satisfaction. This research represents a significant advancement in sludge dewatering strategies. It underscores the pivotal role of innovative decision-making approaches in advancing the field of sludge dewatering methodologies for more sustainable wastewater treatment practices.

关键词： Extracellular polymeric substance Fenton's reagent Fuzzy optimization Pareto front Sludge dewatering Ultrasonication

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