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multiobjective optimization-Based Task Offloading Combined With Power and Resource Allocation in Mobile Edge Computing

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IEEE SYSTEMS JOURNAL 2023年第4期17卷 5738-5749页

作者： Chai, Zheng-yi Yuan, Dong Li, Ya-lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

For mobile devices with limited computing power, it is a challenging problem to meet the demand of low-delay tasks and reduce their energy consumption and cost in mobile edge computing (MEC). In the multidevice and multiserver computing offloading model in MEC, we consider that each device has different computing power and independent tasks with different computational workload, and combine three optimization objectives of task offloading, power allocation, and resource allocation to construct a multiobjective optimization problem. To solve the abovementioned problem, we propose an efficient multiobjective evolutionary algorithm. Compared with the related algorithms, the proposed method can more effectively minimize the computational delay, device energy consumption, and cost. Simulation results show that the offloading revenue of mobile devices has been significantly optimized.

关键词： Index Terms-Mobile edge computing (MEC) multiobjective optimization power assignment resource allocation task offloading

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multiobjective optimization of a staggered-rotor octocopter design based on a surrogate model

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年第1期139卷

作者： Zhu, He Li, Dongdong Nie, Hong Wei, Xiaohui Wei, Yuanzun Nanjing Univ Aeronaut & Astronaut Natl Key Lab Rotorcraft Dynam Key Lab Fundamental Sci Natl Def Adv Design Techno Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Key Lab Fundamental Sci Natl Def Adv Design Techno Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Peoples R China

When designing a novel double-layer staggered-rotor octocopter with improved aerodynamic perfor-mance, the aerodynamic performance and structural parameters will have a complex coupling relation-ship that should be considered in the overall design process. A surrogate-model-based multiobjective optimization method is proposed in this study to optimize novel octocopter structural design schemes considering total thrust, the weight of the propeller and fuselage, the storage volume, and the fuselage volume. This approximation and optimization approach effectively reduces the cost of a large amount of computational fluid dynamics (CFD) calculations in octocopter design and provides a global prediction of octocopter performance. According to the verified optimal solution through CFD simulation, we can see that the surrogate model offers excellent predictive ability, and the prediction accuracy of thrust exceeds 95%. Compared with the initial optimum data, the optimal solution calculated from the optimization could generate 76.74% more thrust, the objective function y is improved by 11.48%, and the objective function a is improved by 322.06%. These results show that the aerodynamic/structural parameter op-timization design method considering the influence of aerodynamic interference for multirotor aircraft proposed in this paper is feasible and exhibits good performance. The methodology proposed in this work could provide guidelines for structural parameter design optimization for double-deck multiple-rotor air-craft design at the preliminary design stage, as well as copter structural design considering multiple-rotor interference effects.& COPY;2023 Elsevier Masson SAS. All rights reserved.

关键词： multiobjective optimization Configuration Octocopter design Surrogate model

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multiobjective optimization of daylighting, energy, and thermal performance for form variables in atrium buildings in China's hot summer and cold winter climate

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ENERGY AND BUILDINGS 2023年第1期297卷

作者： Xiao, Wei Zhong, Wenzhou Wu, Haoran Zhang, Tong Southeast Univ Sch Architecture 2 Sipailou Nanjing 210096 Peoples R China

Building form parameters, including building orientation, exterior shape, and interior space, interactively influence building performance. Nevertheless, few studies have reasonably proposed variables for all these parameters and optimized them simultaneously to establish trade-offs among daylighting, energy, and thermal performance. This study extracts the design variables that have a significant impact on performance, including the orientation, south facade inclination, roof inclination, south/north section aspect ratio of the atrium (SARs/ SARn), east/west section aspect ratio of the atrium (SARe/SARw), and top-to-floor area ratio of the atrium (TFAR). A multiobjective optimization (MOO) framework is proposed for balancing daylighting performance, energy consumption, and thermal comfort, and useful daylight illuminance (UDI), energy use intensity (EUI), and thermal discomfort time percentage (TDP) are used as metrics to assess the interactive relationships between design variables and performance parameters. A building in the hot summer and cold winter (HSCW) zone is selected as an example, a south-by-west orientation, positive south facade inclination and negative roof inclination, large SARs/SARn and SARe/SARw, and small TFAR yield balanced performance. With reference to the initial solution, improvements in EUI could reach 18.67%. Building forms in the HSCW zone can be optimized based on these results, thus providing a good foundation for sustainability.

关键词： Building form variables multiobjective optimization Daylighting performance Energy consumption Thermal comfort

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multiobjective optimization-Based Terrestrial Laser Scanning Layout Planning for Landslide Monitoring

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Zhang, Wendian Chen, Zhiping Huang, Ronggang Dong, Zhen Jiang, Liming Xia, Yuanping Chen, Benfu Wang, Hansheng Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol State Key Lab Geodesy & Earths Dynam Wuhan 430071 Peoples R China East China Univ Technol Fac Geomat Nanchang 330013 Peoples R China East China Univ Technol Fac Geomat Nanchang 330013 Peoples R China East China Univ Technol Key Lab Mine Environm Monitoring & Improving Poyan Minist Nat Resources Nanchang 330013 Peoples R China Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol State Key Lab Geodesy & Earths Dynam Wuhan 430071 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol State Key Lab Geodesy & Earths Dynam Wuhan 430071 Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing 100049 Peoples R China East China Univ Technol Fac Geomat Nanchang 330013 Peoples R China

Terrestrial laser scanning (TLS) is an important means to monitor landslides, and the layout is the key to guarantee captured point clouds with a high quality and low cost. Nevertheless, TLS layout in landslide monitoring is currently determined by user's subjective experience and shows a poor performance in reliability and point accuracy. Therefore, we propose multiobjective optimization-based TLS layout planning for landslide monitoring. 3-D scanning simulation of candidate scan positions is first carried out. A multiobjective optimization problem (MOP) is then constructed according to the requirements of landslide TLS monitoring in the coverage, cost, and point accuracy. Finally, the combination of an improved nondominated sorting genetic algorithm II (NSGA-II) and the Pareto-Edgeworth-Grierson (PEG) algorithm is introduced to solve the MOP and obtain the optimal layout. To test the proposed method, experiments were performed in two landslide sites in Wuhan, China. Results showed that the planned TLS layouts reached a tradeoff among coverage, point accuracy, and the number of TLS scan positions. Comparison experiments based on simulation demonstrated that the constraints fused into NSGA-II dramatically improved the reliability of the planned TLS layout, and the proposed method improved the coverage and point accuracy by 11.62% and 1.52 times compared to the user's experience. Real scanning demonstrated that the proposed method improved the coverage by 10.48% and reduced the deformation error by 0.78-7.66 mm compared to the user's experience. In summary, the proposed method is an efficient approach for TLS layout planning, which can improve the ability of landslide TLS monitoring.

关键词： Terrain factors Layout Monitoring Planning Costs Linear programming optimization Constrained nondominated sorting genetic algorithm II (NSGA-II) landslide multiobjective optimization terrestrial laser scanning (TLS) layout planning

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multiobjective optimization to Optimal Moroccan Diet Using Genetic Algorithm

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International Journal for Engineering Modelling 2023年第1期36卷 67-79页

作者： Ahourag, Abdellah El Moutaouakil, Karim Cheggour, Mouna Chellak, Saliha Baizri, Hicham Sidi Mohamed Ben Abdellah University Technical Sciences Faculty Morocco Sidi Mohamed Ben Abdellah University FPT of Taza Morocco Cadi Ayyad University Faculty of Medicine and Pharmacy Marrakech Morocco

Proper glucose control is designed to prevent or delay the complications of diabetes. Various contexts can lead to a fluctuation of the blood sugar level to a greater or lesser extent. It can he, for example, eating habits, treatment, intense physical activity, etc. The feeding problem interpolated by a minimum cost function is well-known in the literature. The main goal of this paper is to introduce a multiobjective programming model with constraints for the diet problem with two objective functions, the first of which is the total glycemic load of the diet while the second objective function is the cost of the diet. the MOGA (multiobjective Genetic Algorithm) algorithm was used to resolve the proposed model. The experimental results show that our system ([proposed model-MOGA]) is able to produce adequate diets that can settle glycemic load and cost while respecting the patient's requirements. © 2023, University of Split. All rights reserved.

关键词： multiobjective optimization

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multiobjective optimization of roadheader shovel-plate parameters based on improved particle swarm optimization and grey decision

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SCIENCE PROGRESS 2023年第2期106卷 368504231180089页

作者： Li, Qiang Gao, Mengdi Ma, Zhilin Suzhou Univ Sch Mech & Elect Engn Suzhou Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou Peoples R China

The development of tunneling equipment still lags behind, limiting rapid and accurate tunneling and restricting efficient production in coal mines. Thus, improving the reliability and design of roadheaders becomes essential. As the shovel plate is an essential part of a roadheader, improving its parameters can increase the roadheader performance. The parameter optimization of roadheader shovel plate is multi-objective optimization. Because of conventional multiobjective optimization requires strong prior knowledge, often provides low-quality results, and presents vulnerability to initialization and other shortcomings when used in practice. We propose an improved particle swarm optimization (PSO) algorithm that takes the minimum Euclidean distance from a base value as the evaluation criterion for global and individual extreme values. The improved algorithm enables multiobjective parallel optimization by providing a non-inferior solution set. Then, the optimal solution is searched in this set using grey decision to obtain the optimal solution. To validate the proposed method, the multiobjective optimization problem of the shovel-plate parameters is formulated for its solution. Before optimization shovel-plate most important parameters l is the width of the shovel plate l = 3.2 m, beta is the inclination angle of the shovel plate and beta=,19 degrees. When doing optimization, set accelerated factor c(1) = c(2) = 2, population size N = 20, and maximum number of iterations T-max = 100. In addition, speed V was restricted by V = V-imax - V-i min, and inertia factor W was dynamic and linearly decreasing, w(t) = w(min) + w(max)-w(min)/N (N - t), with w(max) = 0.9 and w(min) = 0.4. In addition, r(1) and r(2) were set randomly in [0, 1], while optimization degree eta was set to 30%. And then we executed the improved PSO, obtaining 2000 non-inferior solutions. Apply grey decision to find the optimal solution. The optimal roadheader shovel-plate parameters are l = 3.144 m and b

关键词： Roadheader shovel plate improved particle swarm optimization Euclidean distance base value grey decision multiobjective optimization

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multiobjective optimization of Murta (Ugni molinae Turcz) juice lactic acid fermentation with monocultures and cocultures of Lactobacillus acidophilus and Lactiplantibacillus plantarum

APPLIED FOOD RESEARCH

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APPLIED FOOD RESEARCH 2025年第1期5卷

作者： Gomes-Lobo, C. J. Luna, R. Rodriguez-Machado, A. Perez-Correa, J. R. Franco, W. Pontificia Univ Catolica Chile Dept Chem Engn & Bioproc Avda Vicuna Mackenna 4860 Santiago Chile Ctr Res & Innovat CRI Vina Concha & Toro Pencahue 3550000 Chile

Response surface methodology (RSM) and the desirability function (DF) approach have been widely used for multiresponse optimization in lactic acid fermentation processes. However, multiobjective optimization (MOO) can provide a broader range of optimal cultivation conditions that compromise between multiple objectives. This study applied multiobjective optimization of murta (Ugni molinae Turcz) juice lactic fermentation under monoculture and coculture conditions. The total phenolic content (TPC), Lactic Acid Bacteria (LAB) count, and Lactic Acid (LA) concentration were optimized simultaneously. Decision variables were temperature, initial pH, and inoculation percentage. Several multicriteria decision-making (MCDM) methods were applied to select the optimal compromising responses, considering 80% priority for TPC, 15% for LAB, and 5% for LA. The highest TPC (723 f 15 mg/L) was achieved at 35.3 degrees C, pH 5.9, and 2% (v/v) inoculum in monoculture. The highest LAB count (6.7 f 0.0 log CFU/mL) was obtained in monoculture at 34.6 degrees C, pH 6, and 2.7% (v/v) inoculum. The highest LA (0.48 f 0.03 g/L) was achieved in coculture at 35.6 degrees C, pH 5.9, and 2.1% (v/v) inoculum. The simple additive weighting (SAW) method generated optimal solutions that aligned better with our expected priorities. Experimental validation confirmed that monocultures closely matched predicted optima, yielding higher TPC and LAB values than cocultures. While MOO and DF produced similar Pareto fronts, MOO required less computational time and generated more diverse solutions. These findings highlight the practical utility of MOO-MCDM for designing efficient multiobjective fermentation processes in functional food development.

关键词： multiobjective optimization Multicriteria decision-making NSGA-II Desirability function Lactic acid fermentation

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multiobjective optimization of the Storage Location Allocation of a Retail E-commerce Picking Zone in a Picker-to-parts Warehouse

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ENGINEERING LETTERS 2023年第2期31卷 481页

作者： Wan, Yanchun Wang, Shudi Hu, Yujun Xie, Yanyang South China Univ Technol Dept Elect Commerce Guangzhou 510006 Peoples R China Guangdong Planning Res & Design Acad China Post Gr Guangzhou 510610 Peoples R China

Storage location allocation has a great impact on the utilization of warehouse storage space, operation efficiency, cost and employee well-being. We establish a multiobjective model of storage location optimization of manual picking zones by adopting the principle of high delivery frequency priority, correlation principle and large-capacity priority principle. The intelligent genetic algorithm and particle swarm optimization (IGPSO) algorithm are used to solve the multiobjective model. By using the 3.6 million-order data of a retail e-commerce warehouse to optimize the storage location allocation, the results show that the maximum and average response time, the actual order picking time, the daily walking distance of the picker and other key indicators achieve good results. This research can be well applied to optimize the storage location allocation of similar retail e-commerce warehouses.

关键词： storage location allocation multiobjective optimization picker-to-parts warehouse IGPSO algorithm large volume priority

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multiobjective optimization of railway cold-chain transportation route based on dynamic train information

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2023年 26卷

作者： Liu, Shouchen Zhang, Cheng Fujian Business Univ Sch Business Adm Fuzhou 350012 Peoples R China East China Jiao Tong Univ Sch Transportat & Logist Nanchang 330013 Peoples R China East China Jiaotong Univ 808Shuanggang East StNanchang Econ & Technol Dev Nanchang Jiangxi Peoples R China

Freight train is selected to pick up and hang refrigerated containers according to railway freight train diagram in China. A multiobjective optimization model of railway cold-chain transportation route is established with total, time, and reliability costs as objective functions on the basis of dynamic train information. Different weights of three objective functions are obtained and weighted sum method is used to transform the multiobjective problem into a single-objective problem according to different transport demands of railway cold-chain transportation partici-pants. An example of k short-circuit optimization algorithm based on genetic algorithm (GA) is designed to prove the feasibility and effectiveness of the proposed model. The empirical analysis showed that different transport times can be obtained by adjusting weights of various optimi-zation objectives in the model to meet diverse needs of railway cold-chain transport participants and selecting differentiated shifts and transfer stations on the same route to provide a variety of transportation time limit options. Results of this study can provide guidance to decision makers in choosing railway transportation schemes.

关键词： Railway transportation Cold chain Time window K short circuit multiobjective optimization

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multiobjective optimization of Interpretable Fuzzy Systems and Applicable Subjects for Fast Estimation of Obstructive Sleep Apnea-Hypopnea Severity

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第7期31卷 2225-2237页

作者： Juang, Chia-Feng Pan, Guan-Ren Huang, Wei-Chang Wen, Chih-Yu Wu, Ming-Feng Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan Taichung Vet Gen Hosp Dept Internal Med Div Chest Med Taichung 40705 Taiwan Natl Chung Hsing Univ Coll Med Taichung 402 Taiwan Chung Shan Med Univ Sch Med Taichung 40201 Taiwan Natl Chung Hsing Univ Taichung 402 Taiwan JenTeh Jr Coll Med Nursing & Management Dept Med Technol Miaoli 356006 Taiwan Tunghai Univ Dept Ind Engn & Enterprise Informat Taichung 407224 Taiwan Cent Taiwan Univ Sci & Technol Dept Med Lab Sci & Biotechnol Taichung 40601 Taiwan

This article proposes an interpretable fuzzy estimation system (IFES) for fast estimation of severity of obstructive sleep apnea-hypopnea syndrome (OSAHS) using three easily available physiological variables: neck circumference, waist circumference, and average blood pressure after waking up. The IFES aims to decrease the waiting list of polysomnography so that OSAHS treatment can be performed as soon as possible. The IFES is optimized to achieve the following three objectives: high estimation accuracy, the largest number of applicable subjects, and high model interpretability. The applicable subjects are determined by evaluating the influence of six screening factors, such as smoking, hypertension, and sleep efficiency, on the estimation performance. This article finds solutions of this multiobjective optimization problem using a multiobjective genetic algorithm. A total of 1197 participants are enrolled with the five-fold cross-validation scheme employed to evaluate an estimation performance. Experimental results show that the proposed method successfully finds the influence of different screening factors and the found IFESs outperform different estimation models used for comparison.

关键词： Estimation optimization Medical diagnostic imaging Indexes Hypertension Sleep apnea Fuzzy neural networks Apnea-hypopnea index (AHI) evolutionary fuzzy systems fuzzy systems interpretable AI multiobjective optimization obstructive sleep apnea

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