检索结果-内蒙古大学图书馆

Soft computing approaches for dynamic multi-objective evaluation of computational offloading: a literature review

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第9期27卷 12459-12481页

作者： Khan, Sheharyar Jiangbin, Zheng Ali, Hassan Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

Optimizing computational offloading in Mobile Edge Computing (MEC) environments presents a multifaceted challenge requiring innovative solutions. Soft computing, recognized for its ability to manage uncertainty and complexity, emerges as a promising approach for addressing the dynamic multi-objective evaluation inherent in computational offloading scenarios. This paper conducts a comprehensive review and analysis of soft computing approaches for Dynamic multi-objective Evaluation of Computational Offloading (DMOECO), aiming to identify trends, analyze existing literature, and offer insights for future research directions. Employing a systematic literature review (SLR) methodology, we meticulously scrutinize 50 research articles and scholarly publications spanning from 2016 to November 2023. Our review synthesizes advancements in soft computing techniques, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, as applied to computational offloading optimization within MEC environments. Within this comprehensive review, existing approaches are categorized and analyzed into distinct research lines based on methodologies, objectives, evaluation metrics, and application domains. The evolution of soft computing-based DMOECO strategies is emphasized, showcasing their effectiveness in dynamically balancing various computational objectives, including energy consumption, latency, throughput, user experience, and other pertinent factors in computational offloading scenarios. Key challenges, including scalability issues, lack of real-world deployment validation, and the need for standardized evaluation benchmarks, are identified. Insights and recommendations are provided to enhance computational offloading optimization. Furthermore, collaborative efforts between academia and industry are advocated to bridge the theoretical developments with practical implementations. This study pioneers the use of SLR methodology, offering valuable perspec

关键词： Soft computing Mobile cloud computing Computational offloading Hybrid computing Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Balancing water, power, and carbon: A synergistic optimization framework for mega cascade reservoir operations

引用

RENEWABLE ENERGY 2025年 243卷

作者： Ning, Zhihao Zhou, Yanlai He, Juntao Tang, Chun Xu, Chong-Yu Chang, Fi-John Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Univ Oslo Dept Geosci POB 1047 Blindern N-0316 Oslo Norway Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 106216 Taiwan

This study addresses the critical intersection of renewable energy production and carbon emission reduction in the context of intensified human activities and global climate change by proposing an innovative optimization framework for mega cascade reservoirs. Unlike traditional approaches that often prioritize hydropower output or carbon emissions, our framework uniquely integrates a multi-objective optimization model that simultaneously minimizes carbon emissions, mitigates flood risk, and maximizes hydropower output within the physical constraints of cascade reservoir operations. To assess the performance of various impoundment schemes, we apply the Technique for Order Preference by Similarity to Ideal Solution, demonstrating the model's versatility across different inflow scenarios using seven cascade reservoirs in the Yangtze River as case studies. Our findings reveal that, compared to practical operation scheme, the optimal scheme enhances hydropower output by 5.82 billion kW & sdot;h/a (5.32 %), increases water supply by 2.68 billion m3 (8.00 %), reduces carbon emissions by 17.31 GgC/a (14.66 %), and lowers carbon intensity by 0.63 kgCO2e/MW & sdot;h (15.22 %). This research advances theoretical frameworks for reservoir operations and offers practical implications for policymakers, enabling more informed decision-making to achieve sustainable development goals. The novel integration of water-carbon synergies within reservoir management contributes significantly to the discourse on sustainable energy systems and climate resilience.

关键词： Cascade reservoirs Hydropower optimization Carbon emission reduction multi-objective operation Non-dominated sorting genetic algorithm II (NSGA-II) Sustainable water management

来源：评论

学校读者我要写书评

暂无评论

A fast convergence EO-based multi-objective optimization algorithm using archive evolution path and its application to engineering design problems

引用

JOURNAL OF SUPERCOMPUTING 2023年第16期79卷 18849-18885页

作者： Ghazaan, Majid Ilchi Ghaderi, Pedram Rezaeizadeh, Amirali Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

Time-consuming objective functions are inevitable in practical engineering optimization problems. This kind of function makes the implementation of metaheuristic methodologies challenging since the designer must compromise between the quality of the final solution and the overall runtime of the optimization procedure. This paper proposes a novel multi-objective optimization algorithm called FC-MOEO/AEP appropriate for highly time-consuming objective functions. It is based on an equilibrium optimizer equipped with an archive evolution path (AEP) mechanism. The AEP mechanism considers the evolutionary trajectory of the decision space and anticipates the potential regions for optimal solutions. Besides having a high convergence rate, the newly proposed approach also possesses an intelligent balance between exploration and exploitation capabilities, enabling the algorithm to effectively avoid getting stuck in the local Pareto. To assess the efficacy of the FC-MOEO/AEP, a range of mathematical optimization problems and three real-world structural design problems were employed. In order to gauge the method's performance against other approaches, a novel performance indicator known as convergence speed was introduced and utilized, in addition to standard metrics. The numerical findings demonstrate the robust and consistent performance of the FC-MOEO/AEP in tackling complex multi-objective problems.

关键词： multi-objective optimization Metaheuristic algorithm Evolution path Structural optimization Performance metrics Fast convergence

来源：评论

学校读者我要写书评

暂无评论

Operational synchromodal transport planning methodologies: Review and roadmap

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 194卷

作者： Zhang, Yimeng Tan, Xiangrong Gan, Mi Liu, Xiaobo Atasoy, Bilge Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China Natl Engn Lab Integrated Transportat Big Data Appl Chengdu Peoples R China Delft Univ Technol Dept Maritime & Transport Technol Delft Netherlands Delft Univ Technol Dept Transport & Planning Delft Netherlands

This review aims to explore the potential for synchromodal transport planning at the operational level. Synchromodal transport planning involves the optimization of the movement of freights across multiple transport modes, with the objective of minimizing cost, improving efficiency, and promoting sustainability. Through this review, we provide a roadmap for methodological developments in the area of operational synchromodal transport planning research. The roadmap provides a comprehensive categorization of different fields and their trends. The fundamentals of synchromodal transport planning are evolved to more flexible planning approaches that take practical considerations and multiple objectives into account. Dynamic planning is evolving to become more adaptive and resilient to changing environments. Finally, collaborative planning will continue to integrate both vertical and horizontal collaboration with distributed optimization approaches. With dynamic and collaborative approaches considering preferences, the full potential of synchromodal transport planning can be unlocked towards efficient and sustainable freight transportation.

关键词： Synchromodal transport planning Operational planning multi-criteria optimization Dynamic optimization Collaborative planning

来源：评论

学校读者我要写书评

暂无评论

A multi period portfolio optimization: Incorporating stochastic predictions and heuristic algorithms

引用

APPLIED SOFT COMPUTING 2025年 170卷

作者： Ahmadi, Seyedeh Asra Ghasemi, Peiman German Univ Technol Oman Dept Logist Tourism & Serv Management Muscat Oman Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria

In the field of economics and financial markets, optimal asset allocation strategies are essential for investor satisfaction and success. This paper delves into the complex landscape of multi-period portfolio selection, where the objective is to maximize wealth while minimizing investment risk. The core challenge of this research lies in addressing the complexity and uncertainty inherent in multi-period portfolio selection under stochastic conditions. The study introduces a framework for multi-period portfolio selection, considering N risky assets over T time periods. Stochastic return rates are modeled using a stochastic distribution, with the objective of maximizing wealth under risk constraints. The study presents an empirical case study involving the S&P500 market index, demonstrating the applicability of the proposed approach. Utilizing a random forest model, the paper predicts future returns, incorporating these predictions into a deterministic model via chance constraints. The contributions of the paper are substantial and multifaceted. Firstly, it introduces bankruptcy constraints, providing a more realistic approach to portfolio optimization and addressing an often-overlooked aspect of financial modeling. Secondly, transaction costs, a critical consideration in real-world scenarios, are integrated into the model, significantly enhancing the accuracy and practical relevance of portfolio optimization strategies. Thirdly, uncertainty management is rigorously tackled through stochastic approaches, ensuring the development of robust strategies that can accommodate varying market conditions. The paper also introduces risk-adjusted performance measures, enabling more informed decision-making by considering both risk and returns. Innovatively, this paper employs the Random Forest technique to predict return rates, thereby substantially enhancing the precision of investment predictions. Additionally, the Root System Growth Algorithm adds a heuristic dimension to pro

关键词： multi-Period Portfolio optimization (MPPO) Stochastic chance constraint Random Forest Root System Growth Algorithm (RSGA) Bankruptcy constraints

来源：评论

学校读者我要写书评

暂无评论

An Evolutional Topology optimization Method Based on Kernel Level Set Function

引用

IEEE ACCESS 2025年 13卷 47181-47200页

作者： Sato, Takahiro Watanabe, Kota Igarashi, Hajime Muroran Inst Technol Grad Sch Engn Muroran Hokkaido 0508585 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

Topology optimizations involving evolutionary algorithms are promising approaches to solve practical engineering design problems, since their use of derivation-free algorithms makes them applicable to any design problem. In some evolutional topology optimization methods, structures are determined through the use of spatially smooth parametric functions. This article, first, presents a new parametric level set function using kernel functions as a general formulation of spatially smooth function-based methods. It is called the kernel level set function, which is used to determine the distributions of materials in the design region. By using the evolutionary algorithm to modify the profile of the kernel level set function, topology optimizations can be realized for various design problems, and the shape expression ability of the kernel level set function can be customized by changing the kernel functions. Second, a multi-objective formulation considering an objective and structural complexity is introduced, and an optimization algorithm specialized for solving it is proposed. The combination of the kernel level set function and the present optimization algorithm makes it possible to solve various topology optimization problems, and Pareto fronts with respect to target performance and structural complexity can be obtained. The present method is applied to three topology optimization problems. The numerical results are used to examine the characteristics of the present method for various kernel functions. Furthermore, the present method is applied to an optimization problem of an interior permanent magnet motor for electric vehicles. It is shown that the present method can solve practical design problems that have various objectives and constraint conditions, thanks to the use of a derivation-free evolutionary algorithm.

关键词： Level set Kernel Topology Evolutionary computation Linear programming Complexity theory Electric machines Finite element analysis Shape Reviews CMA-ES electric machine evolutionary algorithms topology optimization

来源：评论

学校读者我要写书评

暂无评论

A data driven sequential learning framework to accelerate and optimize multi-objective manufacturing decisions

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2024年第8期35卷 4087-4112页

作者： Khosravi, Hamed Olajire, Taofeeq Raihan, Ahmed Shoyeb Ahmed, Imtiaz West Virginia Univ Dept Ind & Management Syst Engn Morgantown WV 26505 USA

Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. Our data-driven framework can facilitate more efficient manufacturing choices, which not only minimizes resource usage but also promotes reduced energy consumption and thereby aids in pollution prevention.

关键词： multi-objective bayesian optimization Data-driven decisions Gaussian process Sequential learning Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Heterogenous architected materials: enhancing mechanical performance through multi-objective optimization

引用

ENGINEERING WITH COMPUTERS 2025年第2期41卷 1241-1259页

作者： Arefin, Amit Khatri, Nava Habib, A. K. M. Ahasun Lu, Qiugang Idesman, Alexander Egan, Paul F. Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA Texas Tech Univ Dept Elect & Comp Engn Lubbock TX 79409 USA Southwestern Oklahoma State Univ Dept Chem & Phys Weatherford OK 73096 USA Texas Tech Univ Dept Chem Engn Lubbock TX 79409 USA

Computational approaches are a growing necessity for designing complex architected structures, particularly for multifunctional systems with numerous trade-offs. Architected lattice structures formed from repeating unit cells often face multiple loading scenarios in practical applications which make it difficult to determine the best configuration, especially considering emergent behaviors when combining different unit cells. Here, a non-dominated sorting genetic algorithm (NSGA II) is adopted for dual-objective optimization of architected materials for elastic and shear moduli with heterogeneous unit cell placement. Design inputs consist of two different cubic unit cell topologies, each with a fixed unit cell length of 3.6 mm and beam diameter of 0.8 mm. Designs during optimization were evaluated with finite element analysis and search results demonstrated a non-dominated front for elastic and shear modulus trade-offs with a diversity of designs. Computationally optimized heterogenous design solutions increased the elastic modulus limit by 11% and shear modulus limit by 39% when compared to the homogenous and randomized heterogenous solutions. optimization findings were empirically validated by fabricating 3D printed lattices with mechanical testing of compression and shear moduli. Overall, results provided new strategies for configuring lattice designs that demonstrated advantageous multifunctional performance by optimized heterogenous unit cell placement.

关键词： multi-objective optimization 3D printing Architected materials Design Mechanics Lattices

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic multi-objective optimization with artificial neural networks surrogate modeling for optimal energy-economic performance for CSP technology

ENERGY AND AI

引用

ENERGY AND AI 2025年 20卷

作者： Allouhi, A. Amine, M. Benzakour Aoul, K. A. Tabet USMBA Ecole Super Technol Fes Route ImouzzerBP 2427 Fes Morocco Univ Chouaib Doukkali Fac Sci El Jadida El Jadida 24000 Morocco United Arab Emirates Univ Coll Engn Architectural Engn Dept POB 15551 Al Ain U Arab Emirates

Among CSP technologies, the linear Fresnel reflector (LFR) can provide reliable carbon-neutral electricity for large-scale applications. In this study, the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications, such as solar multiple and full-load thermal storage hours, were examined. Next, artificial neural network (ANN) surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology. Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg-Marquardt training algorithm was adopted. To overcome overfitting, validation and Bayesian Regularization approaches were compared. As training and testing data, 36 geographical sites with various combinations of design parameters were used. Through multi-objective optimization techniques, including the multi- objective Particle-Swarm Optimizer and multi-objective Grey Wolf Optimizer coupled with ANN surrogate modeling, this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria. The study also identified Site 4 (S4) as a promising candidate for optimal balance between the capacity factor (51.05%) and specific cost (5246.71$/kW), showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.

关键词： Artificial neural network Capacity factor Linear fresnel reflector multi-objective optimization Training algorithm Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolutionary optimization of extreme gradient boosting regression models of the internal turning of PEEK tubes

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Vieira, Jessica Tito Pereira, Robson Bruno Dutra Lauro, Carlos Henrique Brandao, Lincoln Cardoso Ferreira, Joao Roberto Univ Fed Itajuba Inst Ind Engn & Management Itajuba MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Sustainable CIMS Dept Mech & Ind Engn Sao Joao Del Rei MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn Sao Joao Del Rei MG Brazil

Internal turning or boring is an attractive machining process for hole enlarging as it presents low costs, good flexibility, and adaptability. The process presents some instability problems due to the high relation length/diameter ratio of the boring bar. To achieve the best process conditions, predictive models must be estimated and optimization must be conducted. This work presents a statistical learning approach for modeling and optimization of the internal turning process in PEEK tubes. Cutting speed, feed rate, and fixture position were considered input parameters. Cross-validation is used for learning and model selection, including k-fold and bootstrap approaches. The results pointed out that the extreme gradient boosting model was the best for all predictors. For R-a the final prediction metrics results were RMSE = 0.1395, MEA = 0.1126, and R-2 = 1.0031, for F-c, RMSE = 1.8609, MAE = 0.9311, and R-2 = 0.9280, and for Ron(t), RMSE = 21.3084, MAE = 17.8053, and R-2 = 0.6562. These results were much superior to the other methods. multi-objective evolutionary optimization was performed considering the extreme gradient boosting models for roughness, total roundness, and cutting force, besides the deterministic model of the material removal rate. The NSGA-II method was selected considering the hypervolume metric. The pseudo-weight approach is used for select high trade-off solutions to be used in practical production scenarios.

关键词： Internal turning Polyetheretherketone Statistical learning Machine learning Extreme gradient boosting multi-objective evolutionary optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：