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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Cao, Hannan Zheng, Xuemei Liu, Zhuang Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Harbin Inst Technol Elect Engn Dept Harbin Peoples R China

ISBN: (纸本)9781538694909

The parameters of LCL filter is an important key in the distributed new energy power generation. It will influence the quality of the grid and influence the development of new distributed generation system. To solve this problem, this paper proposes a multi-objective particle swarm optimization method (H-MOPSO) based on Pareto to optimize the parameters of LCL filter. The paper adopt the passive damping method to solve the resonance problem of the LCL filter. Then the H-MOPSO method is designed by the Pareto adaptive grid method and the adaptive mutation of population diversity. After that the filter parameters are optimized by designing the optimization variables, the objective functions and the constraints. Finally, the proposed method is verified by simulation in MATLAB/Simulink.

关键词： LCL filter passive damping Pareto multi-objective particle swarm optimization

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Research on Performance Predictive Model and Parameter optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3682-3682页

作者： Pan, Yilong Yu, Yaxin Zhou, Junwei Qin, Wenbing Wang, Qiang Wang, Yinghao Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou 310018 Peoples R China

This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device.

关键词： pneumatic drum seed metering device backpropagation neural network multi-objective particle swarm optimization parameter optimization physical property

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multi-objective optimization based on the RSM-MOPSO-GA algorithm and synergistic enhancement mechanism of high-performance porous concrete

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JOURNAL OF CLEANER PRODUCTION 2025年 486卷

作者： Qu, Guanglei Zheng, Mulian Lu, Chuan Song, Jiakang Dong, Dazhi Yuan, Yueming Changan Univ Key Lab Special Area Highway Engn Minist Educ Xian 710064 Shaanxi Peoples R China Beihua Univ Sch Civil Engn & Transportat Jilin 132013 Jilin Peoples R China Shandong Expressway Construct Management Grp Co Lt Jinan 250101 Shandong Peoples R China Shandong High Speed Linteng Highway Co LTD Linyi 273499 Shandong Peoples R China

Porous concrete (PC) can effectively mitigate various environmental problems associated with road space. Exploring high-performance porous concrete (HPPC) is essential to expanding its applications. However, the current single methods for improvement have almost reached a bottleneck, particularly in terms of mechanical properties. Therefore, this research seeks further breakthroughs from synergistic enhancement and multiobjective optimization. The optimization variables were identified through single-factor experiments, and the optimal solutions for the optimization objectives were subsequently obtained using the response surface methodology (RSM). To address the inherent limitation of RSM in delivering only a single optimal solution, this paper proposed a novel RSM-MOPSO-GA hybrid optimization algorithm. Meanwhile, the synergistic enhancement mechanisms were elucidated through microstructural analysis. The results indicate that the individual enhancement effects of basalt fiber (BF), nano-SiO2 (NS), and waterborne epoxy resin (WER) are limited. However, the RSM-based optimization significantly improved the performance of HPPC, with compressive strength and flexural strength increased by 51.4% and 69.8%, respectively, and the permeability coefficient enhanced by 33.8%. Furthermore, the application of the RSM-MOPSO-GA algorithm produced a stable Pareto front containing 50 individuals for users' decision-making. The interaction between WER and NS at the microscale, combined with the reinforcement of BF at the mesoscale, establishes a synergistic enhancement mechanism. The research findings provide both a theoretical foundation and experimental basis for the further application of HPPC. Additionally, it also offers a novel solution to address the challenges of multi-objective optimization in concrete performance.

关键词： Porous concrete High performance Response surface methodology multi-objective particle swarm optimization Genetic algorithm Synergistic enhancement

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Optimal protection coordination for directional overcurrent relays in radial distribution networks with inverter-based distributed energy resources

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 167卷

作者： Li, Jie Feng, Jingtong Huang, Min Liu, Shenquan Wang, Gang Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China Guangdong Power Grid Co Ltd Guangzhou Power Supply Bur Guangzhou 510630 Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China

The optimal setting of directional overcurrent relays ensures the reliable protection and fast fault clearance of radial distribution networks. However, the increasing proliferation of inverter-based distributed energy resources can result in protection coordination failure or high operating times of directional overcurrent relays in radial distribution networks. This paper investigates the adverse impacts of grid-connected inverter-based distributed energy resources that adopt the active/reactive power control strategy on the protection speed, sensitivity and selectivity of directional overcurrent relays in radial distribution networks. A multi-objective optimal protection coordination model is formulated, which incorporates the protection performance of directional overcurrent relays, operation conditions and economics that result from the inverter-based distributed energy resources and power grids. The Pareto-front of the proposed optimal protection coordination problem is obtained by the improved multi-objective particle swarm optimization, where a time-space section evaluation utilizes the Kmeans clustering method to improve the computational efficiency. Furthermore, an analytic hierarchy process and criteria importance through intercrieria correlation weights-based technique for order preference by similarity to ideal solution algorithm is proposed to determine the optimal directional overcurrent relay settings. The developed coordinated framework is applied to a radial distribution network with inverter-based distributed energy resources in MATLAB and PSCAD/EMTDC simulation. The comparative analysis is provided and the effectiveness of the proposed scheme is validated through extensive case studies.

关键词： Directional overcurrent relay Inverter-based distributed energy resources multi-objective particle swarm optimization Protection coordination Radial distribution network

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Optimized Decision-Making for Tunnel Boring Machine Control Parameters

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ROCK MECHANICS AND ROCK ENGINEERING 2025年第5期58卷 5711-5731页

作者： Zhou, Zhenliang Li, Zonglin Tan, Zhongsheng Lei, Ke Zhang, Lilong Beijing Jiaotong Univ Key Lab Urban Underground Engn Minist Educ Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China China Railway 16th Bureau Grp Co LTD Beijing 100018 Peoples R China

Tunnel Boring Machines are widely used in the construction of long tunnels. However, the control parameters in boring are selected by the operators, making it difficult to ensure the stability of the process. To address this issue, an optimization decision-making method for Tunnel Boring Machine control parameters was established. The method consists of three modules: boring performance prediction, multi-objective optimization, and control parameter decision-making. It can output recommended values for total thrust and cutter head rotation speed based on the project's progress and cost requirements. The optimization decision-making method was applied to a water supply tunnel in Xinjiang, China, resulting in an increase in tunneling speed and average single cutter rock-breaking volume by 11.0-15.6% and 4.5-5.3%, respectively. No tunneling stagnation or equipment damage occurred due to improper control parameter selection, proving the method to be feasible and effective. The research results can provide a reference for the decision-making of control parameters in similar projects.

关键词： Tunnel boring machine Control parameter Boring performance Prediction model multi-objective particle swarm optimization Parameter decision

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multi-objective optimization of hybrid battery thermal management design with considering thermal runaway propagation prevention

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APPLIED THERMAL ENGINEERING 2025年 269卷

作者： Li, Meng-Fei Liu, Zhao Yang, Wei-Wei Lin, Y. W. Ji, Kai-Feng Jiang, Zhi-Yuan Xi An Jiao Tong Univ Key Lab Thermofluid Sci & Engn MOE Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China

Maintaining lithium-ion batteries within an ideal temperature range and mitigating the risks of thermal runaway are crucial for electric vehicles. However, few studies have addressed both aspects of battery thermal management systems simultaneously. This study proposes a novel hybrid thermal management system that combines composite phase change material (CPCM) consisting of paraffin and expanded graphite (EG) with separated minichannel plates, and explores its performance under normal and extreme conditions. The thermal runaway propagation (TRP) model and the electrochemical-thermal coupled model are developed to investigate the influence of CPCM on TRP and the thermal management performance at discharge, respectively. The critical distribution of CPCM thickness and the EG mass fraction for TRP prevention is obtained. The results show that the critical mass fraction increases with increasing thickness;two strategies for preventing TRP are identified: a low and a high mass EG fraction. Then, the single and interaction effects of the optimization variables on the maximum temperature, temperature difference, and pressure drop are analyzed at 2C discharge. Furthermore, a multi-objective optimization framework based on the surrogate model is constructed to optimize comprehensive thermal performance while satisfying the critical safety condition. Finally, the Pareto optimal solutions satisfying the critical safety condition are obtained through the multi-objective optimization algorithm. The optimized solution shows a 4.37% reduction in maximum temperature, a 2.07% reduction in temperature difference, and a 75.13% decrease in pump energy consumption compared to the original design. This work considers two scenarios in the optimization and the optimized results satisfy both requirements.

关键词： Thermal runaway propagation multi-objective particle swarm optimization Electrochemical-thermal coupled model Composite phase change material

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An innovative dual-phased synergistic energy management approach for WSNs using enhanced sleep/awake scheduling and adaptive routing process

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SIMULATION MODELLING PRACTICE AND THEORY 2025年 142卷

作者： Roberts, Michaelraj Kingston Jeevanandham, S. Lloret, Jaime Dahan, Fadl Sri Eshwar Coll Engn Dept ECE Coimbatore India Sri Ramakrishna Engn Coll Dept Informat Technol Coimbatore India Univ Politecn de Valencia Inst Invest La Gest Integrada Zonas Costeras C-Paraninf1 Grao Gandia Valencia 46730 Spain Prince Sattam bin Abdulaziz Univ Coll Business Adm Hawtat Bani Tamim Dept Management Informat Syst Al Kharj 11942 Saudi Arabia

Wireless Sensor Networks (WSNs) have established themselves as one of the essential technologies in various applications, yet they face significant challenges due to their limited energy resources. To overcome this shortcoming, this work introduces an innovative dual-phased synergistic energy management approach that integrates enhanced sleep/awake scheduling based on multi-objective particle swarm optimization with Crowding Distance (MOPSO-CD) and Reservoir Computing (RC) based adaptive routing for optimizing energy consumption using dynamic real time-based node state adjustment mechanism. Experimental outcomes obtained through comprehensive simulations indicate that our proposed methodology achieves up to 32 % reduction in energy consumption per node, a 50 % improvement in extending network lifetime, and a 11 % enhancement in Packet Delivery Ratio (PDR) compared to state-of-the art algorithms. Additionally, the proposed method minimizes the computational overhead by 40 % which ensures reliability in dynamic environmental conditions. This outstanding performance is attributed to the intelligent integration of RC-driven energy predictions with adaptive routing and optimized clustering, which offers significant advancement in energy management strategies for WSNs, paving the path for sustainable and reliable network deployment.

关键词： multi-objective particle swarm optimization with crowding distance (MOPSO-CD) Reservoir Computing (RC) Adaptive routing Sleep/Awake scheduling Synergistic Energy Management Optimized Clustering

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An optimization Method for multi-Functional Radar Network Deployment in Complex Regions

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REMOTE SENSING 2025年第4期17卷 730-730页

作者： Han, Yi Li, Xueting Xu, Xiangliang Zhang, Zhenxing Zhang, Tianxian Yang, Xiaobo Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China

This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics of polygonal deployment regions to enhance deployment efficiency. Specifically, for the aforementioned complex deployment regions, a region decomposition approach based on convex partitioning is proposed. This approach allows for the decomposition of complex regions into multiple non-overlapping convex subregions. Moreover, for convex deployment regions or subregions, we propose a coordinate transformation approach to eliminate the constraints introduced by the shape of the convex region. By combining the above approaches, we introduce a novel MOPSO based on decomposition and transformation, named MOPSO-DT. This algorithm aims to optimize MFRN deployment in these challenging environments. Experimental results demonstrate the superiority of the MOPSO-DT algorithm over two existing algorithms across a variety of deployment cases, highlighting its enhanced efficiency, effectiveness, and stability. These findings indicate that the proposed algorithm is well suited for optimizing MFRN deployment in complex, irregular regions, offering significant improvements in performance compared to conventional methods.

关键词： multi-functional radar network deployment multi-objective particle swarm optimization complex deployment region deployment region decomposition coordinate transformation

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Research on Container multimodal Transportation multi-objective Path optimization From Hinterlands to Shanghai Port

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IEEE ACCESS 2025年 13卷 32794-32807页

作者： Huang, Chuan Sun, Hanbing Liu, Changjian Zhang, Xiaoqing Gao, Tianhang Tian, Jia Minist Transport Transport Planning & Res Inst Beijing 100028 Peoples R China Digital Lab Integrated Transportat Planning Beijing 100028 Peoples R China Wuhan Univ Technol Wuhan 430062 Peoples R China

Given Shanghai Port's proximity to the sea and the Yangtze River, this article investigated the optimization of Shanghai Port's foreign trade container multimodal transport path. First and foremost, because the range of the port hinterland generating container is large, Sichuan Province, Chongqing Municipality, Hubei Province, Hunan Province, Jiangxi Province, Anhui Province, and Jiangsu Province are chosen as the major container generating hinterland. And the container intermodal transportation network is centered on these cities and provinces. Then, using minimizing total transportation cost and total transportation time as objective functions, a multi-objective container multimodal transportation path optimization model is built. The improved multi-objective particle swarm approach is used to solve the mathematical mode. Finally, the efficient multimodal container transportation routes from Yibin, Luzhou, Chongqing, Yi-chang, Wuhan, Jiujiang, and Yueyang to Shanghai Port are identified. The acquired results are used to discuss the port competitiveness and freight organization games. It closes by verifying the usefulness of the approaches and models presented in this study. It is intended that the study presented in this paper will serve as a model and method reference for optimizing container multimodal transportation patterns.

关键词： Transportation Containers Seaports Costs optimization Biological system modeling Linear programming optimization models Roads Pricing Shanghai port container multimodal transportation path optimization multi-objective particle swarm optimization

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optimization of Sustainable Vehicle Routing Problem Taking into Account Social Utility and Employing a Strategy with multiple objectives

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1631-1658页

作者： Gouraji, R. Ebrahimi Soleimani, H. Najafi, B. Afshar Islamic Azad Univ Dept Ind Engn Qazvin Branch Qazvin Iran

In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle *** primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social *** achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route *** results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustain

关键词： Exchange Locations Vehicle Routing Problem Algorithms Non-dominated Sorting Genetic Algorithm-II multi-objective particle swarm optimization Metaheuristic Time Constraint

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