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Research on Balanced Allocation of English Educational Resources Based on Multi-objective Optimisation and NSGA-II

JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2025年第2期24卷

作者： Liu, Dongyang Henan Univ Anim Husb & Econ Sch Foreign Languages Zhengzhou 450046 Peoples R China

With the in-depth development of globalisation, English, as an international common language, is of great importance to the reasonable allocation of its educational resources for the improvement of national educational equity and overall educational quality. However, there has long been a serious imbalance in the allocation of English education resources in China. In terms of resource allocation between urban and rural areas and different schools, differences in the number of teachers, teaching facilities, teaching materials and other aspects lead to gaps in the opportunities and quality of English education for students. This imbalance in resource allocation not only affects educational equity, but also limits students' English learning outcomes. Therefore, this paper proposes an optimal allocation method of English educational resources based on multi-objective optimisation (MOO) and non-dominated sorting genetic algorithm (NSGA-II). This method aims to maximise the utilisation rate of educational resources, narrow the gap between urban and rural areas and schools, and promote educational equity and efficient use of resources by optimising multiple objectives at the same time, such as teacher allocation, balanced use of teaching facilities and fair distribution of teaching materials resources. The experimental results show that the proposed method has a high degree of fit on two different datasets, reaching 94.58% and 96.87%, respectively, and the resource balance is greatly improved in the process of resource allocation. In addition, the algorithm has high operating efficiency under large-scale data, and the training time can be stabilised at 57.84 s when the sample size reaches 24,200. The experiment also shows that the application of this method in districts and counties significantly reduces the fluctuation of educational resources and optimises the allocation level of educational resources in each district and county. The research provides a new way to solve

关键词： Balanced distribution education resources English multi-objective optimisation non-dominated sorting genetic algorithm

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Leveraging genetic algorithms and neural networks to optimise a UAV support arm for stiffness and weight efficiency

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Materials & Design 2025年

作者： Iuri H. Müller José Humberto S. Almeida Sandro C. Amico Maikson L.P. Tonatto Mechanics of Materials and Structures Group Federal University of Santa Maria Cachoeira do Sul Brazil Department of Mechanical Engineering LUT University Lappeenranta Finland PPGE3M/PROMEC Federal University of Rio Grande do Sul Porto Alegre Brazil

The increasing demand for lightweight and high-performance structures in aerospace applications has spurred the development of advanced design frameworks for 3D-printed composite components. This study proposes a multi-objective optimisation approach that integrates the non-dominated sorting genetic algorithm II (NSGA-II) with an Artificial Neural Network (ANN) to optimise the design of a UAV support arm. The dual objectives are to minimise weight and maximise stiffness. A finite element (FE) database is first constructed using four geometric parameters: height, width, lattice element count, and thickness; under combined thrust and torsional loading. This is expanded with three additional parameters − edge thickness, lower reinforcement thickness, and upper reinforcement width − for ANN training. The ANN predicts mass and stiffness with over 99% accuracy while reducing computational time by 99% compared to full FE simulations. NSGA-II then generates a Pareto front, from which an optimal design is selected and fabricated using fused filament fabrication (FFF) with polylactide (PLA). The final prototype achieves a 42% weight reduction and a 51% increase in specific stiffness relative to the baseline, demonstrating the potential of the proposed framework for solving complex composite design problems.

关键词： Multi-objective optimisation non-dominated sorting genetic algorithm Artificial neural network Composite structures Additive manufacturing

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Review of hybrid electric powered aircraft,its conceptual design and energy management methodologies

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Chinese Journal of Aeronautics 2021年第4期34卷 432-450页

作者： Ye XIE Al SAVVARISAL Antonios TSOURDOS Dan ZHANG Jason GU Intelligent Robot Research Centre Zhejiang LabHangzhou311100China School of Aerospace Transport and ManufacturingCranfield UniversityBedford MK430ALUnited Kingdom York University North YorkToronto M3J 1P3Canada Dalhousie University HalifaxNova Scotia B3H 4R2Canada

The paper overviews the state-of-art of aircraft powered by hybrid electric propulsion *** research status of the design and energy management of hybrid aircraft and hybrid propulsion systems are further *** first contribution of the review is to demonstrate that,in the context of relatively underdeveloped electrical storage technologies,the study of mid-scale hybrid aircraft can contribute the most to both theoretical and practical ***,the profits and potential drawbacks of applying hybrid propulsion to mid-scale hybrid airplanes have not been thoroughly ***,as summed in the overview of design methodologies,the multi-objective optimization transcends the single-objective *** potential of the hybrid propulsion system can be thoroughly evaluated in only one optimization run,if several objectives optimized *** there are few researches covering the conceptual design of hybrid aircraft using multi-objective *** review of the most popular energy management strategies discloses the third research gap—current methodologies favoured in hybrid ground vehicles do not consider the aircraft ***,both non-causal and causal energy management are needed for performing a complicated flight mission with several sub-tasks.

关键词： Aircraft Convex optimization Energy management Fuzzy logic control Hybrid electric propulsion system non-dominated sorting genetic algorithm Sizing

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Multiobjective optimization scheme for industrial synthesis gas sweetening plant in GTL process

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Journal of Natural Gas Chemistry 2011年第1期20卷 99-109页

作者： Alireza Behroozsarand Akbar Zamaniyan Department of Chemical Engineering Islamic Azad University Department of Natural Gas Conversion Gas Research Division Research Institute of Petroleum Industry (RIPI)

In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming, and preventing unsuitable accidents, the optimization could be performed by a computer program. In this paper, simulation and parameter analysis of amine plant is performed at first. The optimization of this unit is studied using non-dominated sorting genetic algorithm-II in order to produce sweet gas with CO 2 mole percentage less than 2.0% and H 2 S concentration less than 10 ppm for application in Fischer-Tropsch synthesis. The simulation of the plant in HYSYS v.3.1 software has been linked with MATLAB code for real-parameter NSGA-II to simulate and optimize the amine process. Three scenarios are selected to cover the effect of （DEA/MDEA） mass composition percent ratio at amine solution on objective functions. Results show that sour gas temperature and pressure of 33.98 ？ C and 14.96 bar, DEA/CO 2 molar flow ratio of 12.58, regeneration gas temperature and pressure of 94.92 ？ C and 3.0 bar, regenerator pressure of 1.53 bar, and ratio of DEA/MDEA = 20%/10% are the best values for minimizing plant energy consumption, amine circulation rate, and carbon dioxide recovery.

关键词： amine plant multiobjective optimization non-dominated sorting genetic algorithm amine circulation rate

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Multi-objective optimization of the solar absorptivity distribution inside a cavity solar receiver for solar power towers

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SOLAR ENERGY 2017年 158卷 247-258页

作者： Wang, Kun He, Ya-Ling Li, Peiwen Li, Ming-Jia Tao, Wen-Quan Xi An Jiao Tong Univ Sch Energy & Power Engn Minist Educ Key Lab Thermofluid Sci & Engn Xian 710049 Shaanxi Peoples R China Univ Arizona Dept Aerosp & Mech Engn Tucson AZ 85721 USA

The solar flux distribution on the receiver of a solar power tower is usually not uniform, which can cause a number of problems for the energy efficiency and system safety, particularly, the local hot spot and the thereby caused thermal stress and thermal deformation. Therefore, homogenization of the solar flux distribution is critical and important. The objective of the present study is to homogenize the solar flux distribution while keeping the optical loss (reflection loss) as low as possible through optimization of the distribution of the solar absorptive coating. An integrated approach coupling the Monte-Carlo ray tracing method and the Gebhart method is applied to simulate the process of the solar radiation transfer in the solar power system. The multi objective optimization of the distribution of solar absorptivity is performed by using the non-dominated sorting genetic algorithm. The following conclusions are drawn from the study. (1) The improvement of the uniformity of the distribution of solar flux can lead to more reflection loss due to the fact that more solar energy is distributed on the position with greater view factor to the aperture;(2) The Pareto optimal front obtained from the multi-objective optimization provides the trade-off between the non-uniformity of the solar flux distribution and the reflection loss. (3) The optimal solar absorptivity distribution provided by the Pareto optimal front can significantly flatten the solar flux distribution at a minimum cost of optical loss. (4) The optimal distribution of the solar absorptivity is approximately opposite to the distribution of solar flux projected onto the active surfaces from the heliostat fields.

关键词： Solar power tower Optimal solar absorptivity distribution Solar flux distribution non-dominated sorting genetic algorithm

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A quality of service distributed optimizer for Cognitive Radio Sensor Networks

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PERVASIVE AND MOBILE COMPUTING 2015年 22卷 71-89页

作者： Salem, Tarek M. Abdel-Mageid, Salah Abd El-Kader, Sherine M. Zaki, Mohamed Elect Res Inst Comp & Syst Dept Cairo Egypt Al Azhar Univ Comp & Syst Dept Cairo Egypt

In Cognitive Radio Sensor Networks (CRSNs), a sensor node is provided with a cognitive radio unit to overcome the problem of frequency spectrum being crowded. Sensor nodes sense frequency gaps for Primary Users (PUs) to work as Secondary Users (SUs). However, Quality of Service (QoS) requirements for sensor nodes such as maximizing throughput and minimizing transmission power conflicts with minimizing interference between sensor nodes and PUs. Existing works have optimized QoS parameters considering frequency interference problem using genetic algorithms (GA) and Simulating Annealing (SA). In this paper, a distributed optimizer for CRSNs based on advanced multi-objective evolutionary algorithms named non-dominated sorting genetic algorithm (NSGA-II) has been proposed. A set of accurate fitness functions for NSGA-II implementation that fully control evolution of the algorithm have been employed. To the best of our knowledge, there is no published research in CRSN that contains all these intrinsic fitness functions in one system model. Simulation results show that the proposed optimizer can work as a distributed solution for CRSNs because it achieves a minimum number of iterations and minimum coverage time to reach an optimal solution compared to GA and SA. Such minimization matches the energy requirement for the underlying sensor nodes. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Cognitive Radio Sensor Networks Multi-objective optimization genetic algorithms non-dominated sorting genetic algorithm Simulating Annealing

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Cutting parameter optimization for machining operations considering carbon emissions

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JOURNAL OF CLEANER PRODUCTION 2019年 208卷 937-950页

作者： Zhou, Guanghui Lu, Qi Xiao, Zhongdong Zhou, Ce Tian, Changle Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China

With the aggravation of global warming and implementation of carbon policies such as carbon tax and carbon labeling, manufacturing enterprises have to cope with the stress from eco-environment and production cost. Cutting parameters in the part machining process have a huge impact on energy and material consumption, cutting time and economy of manufacturing system. The traditional cutting parameters are always selected based on human experience and handbooks, which lacks the attention on energy and material saving. In this paper, a cutting parameter optimization method for machining operations considering carbon emissions is proposed to balance cutting indexes, i.e., carbon emissions, cutting time and cutting cost, in the part machining process. Firstly, the numerical relationships between cutting parameters and cutting indexes are analyzed. Particularly, considering the processing capacity of a workshop and machining features, formalized tables named c-PBOM-W (Carbon emissions integrated Process Bill of Material for Workshop) and c-PBOM-P (Carbon emissions integrated Process Bill of Material for Part) are designed to evaluate carbon emissions easily. Then, a multi-objective parameter optimization model is proposed. By introducing non-cooperative game theory, an improved algorithm, non-cooperative Game theory Integrated NSGA-II (NG-NSGA-II), is developed to solve the model. Finally, a cylindrical turning is taken as a case to illustrate the rationality of the proposed cutting parameter optimization method. The simulation results show that the NG-NSGA-II has a better performance than NSGA-II. Thus, the proposed method could provide the optimal cutting parameters in the part machining process to decrease carbon emissions, cutting time, and cutting cost. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Carbon emissions Cutting parameters Multi-objective optimization non-cooperative game theory non-dominated sorting genetic algorithm

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Multi-objective modelling and optimal parameter selection of a multi-pass milling process considering uncertain milling stability constraint

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第9-10期120卷 6225-6240页

作者： Deng, Congying Shu, Jie Ma, Ying Lu, Sheng Zhao, Yang Miao, Jianguo Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China Chongqing Univ Posts & Telecommun Inst Adv Study Chongqing 400065 Peoples R China

Optimizing machining parameters and pass numbers under machining stability constraint is important for manufacture engineers planning a multi-pass milling operation. Considering that milling stability varies with the machine tool dynamics when the tool and workpiece position changes, a generalized regression neural network (GRNN) is adopted to obtain the position-dependent stability constraint. Then a multi-objective optimization model of a multi-pass milling is established, where the objectives are the total cutting time (T-c) and surface roughness (R-a) and the variables are the machining position and cutting parameters for each pass. A non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the multiobjective optimization model for providing an optimal Pareto front, from which one satisfactory solution well balancing the T-c and R-a is selected by combining the entropy-weighted algorithm (EWA) and technique for ordering preferences by similarity to ideal solution (TOPSIS). A case study was carried out to establish a multi-objective optimization model after the milling stability constraint and surface roughness were predicted with the aid of GRNN and back-propagation neural network (BPNN), respectively. An ideal solution containing optimal machining position and cutting parameters was solved using NSGA-II, EWA, and TOPSIS, which was compared with solutions of two mono-objective models for optimizing T-c and R-a, respectively, to validate its feasibility. The observed stability and acceptable R-a value of the milling test under the ideal solution also indicated that the proposed optimization method can realize a trade-off between two conflict objectives and fascinate the process planning of a multi-pass milling.

关键词： Multi-objective optimization Multi-pass milling Position-dependent milling stability Artificial neural network non-dominated sorting genetic algorithm

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Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2011年第12期25卷 2971-2977页

作者： Farmani, Mohammad Reza Jaamialahmadi, Abdolrahman Babaie, Meisam KN Toosi Univ Technol Dept Aerosp Engn Tehran Iran Ferdowsi Univ Mashhad Fac Engn Dept Mech Engn Mashhad Iran Queensland Univ Technol Dept Build Environm & Engn Brisbane Qld 4001 Australia

In this study, force and moment balance of a planar four-bar linkage is implemented using evolutionary algorithms. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects, independent of input angular velocity. A proposed multiobjective particle swarm optimization, and non-dominated soiling genetic algorithm II are applied to minimize two objective functions subject to some design constraints. The applied algorithms produced a set of feasible solutions called pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is utilized to select the best solution among the obtained pareto solutions. The results show that optimal solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.

关键词： Force and moment balance Four-bar linkage Multiobjective particle swarm optimization non-dominated sorting genetic algorithm Fuzzy decision maker

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A dynamic dispatching problem to allocate relief vehicles after a disaster

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ENGINEERING OPTIMIZATION 2021年第11期53卷 1999-2016页

作者： Gharib, Mehrdad Fatemi Ghomi, Seyyed Mohammad Taghi Jolai, Fariborz Amirkabir Univ Technol Dept Ind Engn Tehran Iran Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the model, an exact approach called the -constraint method is applied to the problem. Since this problem belongs to the class of NP-hard problems, two metaheuristic algorithms, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective imperialist competitive algorithm (MOICA), are applied to large-scale problems. The performance of the algorithms is evaluated using computational experiments. Finally, the model is applied in a real-life case study for an expected earthquake in Iran and several managerial insights are extracted.

关键词： Disaster emergency medical services epsilon-constraint method non-dominated sorting genetic algorithm multi-objective imperialist competitive algorithm

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