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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Zhang, Yanyu Zeng, Peng Zang, Chuanzhi Chinese Acad Sci Shenyang Inst Automat Key Lab Networked Control Syst Shenyang Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781479987306

To implement demand response in residential sector and facilitate the integration of renewable resources and plug-in electric vehicles in future smart grid, this paper proposes a framework of home energy management system (HEMS) and a* optimization algorithm for it based on improved artificial bee colony. The algorithm schedules the operations of schedulable home appliances according to electricity price, forecasted outdoor temperature and renewable power output, and user preferences to minimize user's electricity cost. The effectiveness of the algorithm is verified by simulations, and the electricity cost can be reduced by 47.76%.

关键词： smart grid demand response home energy management system artificial bee colony optimization algorithm

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optimization algorithm of Manipulator Robot Performances 4

Optimization Algorithm of Manipulator Robot Performances

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4th IEEE Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Belaidi, Abderrahmane Belaidi, Hadjira Univ Mhamed Bougara Boumerdes Fac Chem & Hydrocarbons Boumerdes 35000 Algeria UMBB Univ IGEE Signals & Syst Lab Boumerdes 35000 Algeria Adv Technol Dev Ctr CDTA Algiers 16303 Algeria

ISBN: (纸本)9781479936694

One aim of controlling a manipulator robot is to maximize its performances such as accuracy, speed, time etc.. However, limiting the power of actuators causes a limitation of their generalized accelerations and velocities;this is due to the high inertial forces, which create dangerous voltages at the machines elements. This paper propose an algorithm to solve the problem of maximizing the manipulator performances considering that the end-effector position and orientation is characterized by a 6x1 vector i.e., six degrees of freedom;moreover, the need to move the end-effector from the start position to the target with a minimum time, without violating its boundaries. The presented solution is well suited to this context. It is optimal with respect to time constraints, and it allows a direct calculation.

关键词： manipulator robot optimization algorithm speed acceleration minimal time

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Predictivea* optimization algorithm for beam combination systems based on adaptive fiber optics collimators

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OPTICS AND LASERS IN ENGINEERING 2022年 148卷 106753-106753页

作者： Huang, Guan Lv, Guoyun Fan, Yangyu Geng, Chao Li, Xinyang Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China Chinese Acad Sci Key Lab Adapt Opt Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China

Beam combining of fiber lasers based on adaptive fiber optics collimator (AFOC) plays an important role in the laser propagation applications, whereas the system performance is severely affected by the target occlusion during the beam's long-range atmosphere transmission. In this paper, the sources, characteristics, and potential impacts of the target occlusion were analyzed for the first time, and the predictive stochastic parallel gradient descent (P-SPGD) algorithm was proposed to solve this problem. The basic principle of P-SPGD was to monitor the target occlusion in real-time, and then dynamically choose whether to use the observed gradient or the predicted gradient to update the control voltages of AFOCs. Beam combining experiments based on three AFOCs were conducted in the free space with a distance of 40 m. The results showed that when the moving target was obscured for 3 s, the AFOCs based on the traditionala* optimization algorithm had lost track of the target, whereas the P-SPGD algorithm can detect this emergency with a probability of 98.8%, and accurately model the motion state of the target. As a result, the AFOCs can still achieve stable target tracking during the period of target occlusion, and keep the combined beams from divergence.

关键词： Laser beam combining optimization algorithm Model predictive control Target occlusion Adaptive fiber optics collimator

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Thea* optimization algorithm of the Forced Current Cathodic Protection Base on Simulated Annealing

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algorithmS 2019年第4期12卷 83-83页

作者： Song, Chunfeng Wang, Mei Qin, Xuebin Wang, Pai Liu, Bao Xian Univ Sci & Technol Coll Elect & Control Engn Xian 710054 Shaanxi Peoples R China

The grounding grid of a substation is important for the safety of substation equipment. Especially to address the difficulty of parameter design in the auxiliary anode system of a grounding grid, an algorithm is proposed that is ana* optimization algorithm for the auxiliary anode system of a grounding grid based on improved simulated annealing. The mathematical model of the auxiliary anode system is inferred from the mathematical model of cathodic protection. On that basis, the parameters of the finite element model are optimized with the improved simulated annealing algorithm, thereby the auxiliary anode system of a grounding grid with optimized parameters is structured. Then the algorithm is proven as valid through experiments. The precision of the optimized parameters is improved by about 1.55% with respect to the Variable Metric Method and the Genetic algorithm, so it can provide a basis for parameter design in the auxiliary anode system of a grounding grid.

关键词： simulated annealing auxiliary anode system grounding grid optimization algorithm finite element model

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optimization algorithm for Home Energy Management System Based on Artificial Bee Colony in Smart Grid

Optimization Algorithm for Home Energy Management System Bas...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Yanyu Zhang Peng Zeng Chuanzhi Zang Key Lab. of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479987313

关键词： Smart grid Demand response Home energy management system Artificial bee colony optimization algorithm optimization algorithms Smart grids Demand-Responsive bee colonies Energy management systems Domestic architecture electricity costs Bee User preferences

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Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control

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ENERGY 2025年 324卷

作者： Shen, Lian Zhou, Pinhan Han, Yan Mi, Lihua Xu, Wenxiang Xu, Jialu Changsha Univ Sch Civil Engn Changsha 410022 Peoples R China Changsha Univ Sci & Technol Hunan Prov Res Ctr Safety Control Technol & Equipm Changsha 410004 Peoples R China Wenzhou Univ Technol Coll Architecture & Energy Engn Wenzhou 325035 Peoples R China

Wind farm layout optimization is essential for improving power generation efficiency and reducing operational costs in wind farms. This study develops a multi-turbine wake superposition model incorporating turbine yaw effects, based on a three-dimensional polynomial wake model. Dynamic weight and Levy flight strategies are employed to enhance the sparrow search algorithm (SSA) for layout optimization. The proposed wake model is validated with experimental data, and the superiority of DLSSA is confirmed through comparisons with traditional algorithms. Parameter analysis of active yaw strategy is conducted using two tandem wind turbines. Integrating DLSSA with the wake model, layout optimization considering height variation and active yawing strategies is investigated using dimensionless annual energy production (DAEP) as the objective function. Simulation data suggests that optimal total power output is attained when two wind turbines are positioned in a tandem configuration, with the upstream turbine set at a yaw angle of 15 degrees. Incorporating height variation and active yaw control significantly enhances the total power output of wind farms. Implementing these strategies in layout optimization can increase total power output by 1.32 %-10.86 % compared to alternative layouts. Notably, joint optimization surpasses sequential optimization, resulting in a 1.70 % higher total power output.

关键词： Three-dimensional wake model Layout optimization Active yawing control optimization algorithm

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Artificial rabbits optimization-based motion balance system for the impact recovery of a bipedal robot

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ADVANCED ENGINEERING INFORMATICS 2025年 63卷

作者： Kuo, Ping-Huan Yang, Wei-Cyuan Lin, Yu-Sian Peng, Chao-Chung Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI Chiayi 62102 Taiwan Natl Cheng Kung Univ Dept Aeronaut & Astronaut Tainan 701401 Taiwan

Research on the control of bipedal robots has predominantly focused on ensuring stability and balance during locomotion, often neglecting the robot's ability to respond to unexpected external disturbances. In the present study, an algorithm is proposed to enable humanoid robots to maintain balance when they experience external impacts. In evaluation experiments, a robot was placed on flat surfaces and sloped terrain, where it experienced impacts from five angles. To evaluate the robot's stability, data were collected before, during, and after each impact. The study utilized the artificial rabbits optimization (ARO) algorithm to optimize parameters and trained the robot's control model by using a five-layer multilayer perceptron (MLP) neural network. Notably, the joint use of ARO and MLP yielded computational savings relative to conventional reinforcement learning methods. The proposed hybrid approach allowed the robot to adapt quickly to external forces and maintain balance effectively. The findings of this research hold considerable promise for enhancing the practical applications of bipedal robots in real-world scenarios, where unpredictable forces or impacts are common. By improving a robot's ability to react dynamically and maintain balance, the proposed method enables humanoid robots to operate in highly challenging and dynamic environments, such as those associated with disaster response, industrial tasks, or everyday human interaction, without falling because of unexpected disturbances. Thus, the present study contributes to the field of humanoid robotics by addressing real-world challenges and providing a robust solution for impact resistance.

关键词： Humanoid robot Intelligent control Artificial rabbits optimization (ARO) Motion balance optimization algorithm Gait pattern generator

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Tolerance-cost optimization for multi-cabin spacecraft assembly considering form errors

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年

作者： Yang, Jiangxin Wu, Chengjin Shao, Nan Cao, Yanlong Anwer, Nabil Cao, Yanpeng Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou Peoples R China Beijing Inst Technol Sch Mech Engn Beijing Peoples R China Univ Paris Saclay ENS Paris Saclay LURPA 4 Ave Sci Gif Sur Yvette France

Tolerance-cost optimization plays an important role in trade-off between deviation reduction and manufacturing cost. However, very few studies focus on the assembly accuracy of the cabin using a more efficient tolerance allocation considering both quality and cost issues. Therefore, this paper proposes a tolerance-cost optimization method for multi-cabin assembly of spacecrafts based on the cloud model based genetic algorithm (CGA). To improve the accuracy of tolerance analysis, a comprehensive assembly accuracy analysis method is used, composed of tolerance representation and propagation. For tolerance representation, skin model shape and small displacement torsor theory are used to respectively represent the form and position errors. For tolerance propagation, the integrated Jacobian-skin model shapes model is used to compute the accumulative results of deviation. Simulation results demonstrate the capability of the proposed method to predict the assembly accuracy of cabins with non-ideal surfaces. To improve the efficiency of the optimization, CGA is utilized as an optimization technique. Compared with traditional optimization methods, simulation analysis shows superior performance of parameter optimization in the problem of tolerance-cost optimization of multi-cabin spacecraft.

关键词： Assembly tolerance analysis tolerance allocation spacecraft cabin optimization algorithm skin model shape

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A Hybrid K-Means plus plus and Particle Swarm optimization Approach for Enhanced Document Clustering

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IEEE ACCESS 2025年 13卷 48818-48840页

作者： Hassan, Eisha Malik, Fazila Khan, Qazi Waqas Ahmad, Nadeem Sardaraz, Muhammad Karim, Faten Khalid Elmannai, Hela Iqra Univ Islamabad Dept Comp & Technol Islamabad 44000 Pakistan Jeju Natl Univ Dept Comp Engn Jeju Si 63243 South Korea COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

Document Clustering has attracted the interest of many researchers who have created several solutions to this problem by combining different techniques, models, and algorithms. While famous for its simplicity, the most commonly used algorithm, K-Means, suffers from issues such as finding the optimal value for k and random initialization of the centroids. In this paper, we propose a hybrid methodology combining K-Means++ with the metaheuristic algorithm PSO to overcome the challenges of both these algorithms. K-Means++ is a smart initialization technique that selects clusters based on probability function so that they are further away from each other. Two methods, Elbow Analysis, and Silhouette Score, have been used to find the optimal value of k. The research also attempts to assess the results of the best-performing feature extraction techniques and create a combined approach using their average. Although the combined approach did not outperform the existing methods, it paved the way for further advancement and exploration of merging feature extraction techniques. The experiments were performed on four datasets: 20 Newsgroups, Reuters, WebKB, and Wine, and the results were evaluated using three evaluation metrics: Purity, Silhouette Score, and Jaccard Index. The proposed approach achieved a Purity of 0.95, 0.91. 0.86, 0.89, the Silhouette Score was measured to be 0.5, 0.05, 0.05, 0.59, and the Jaccard Index was reported to be 0.04, 0.04, 0.05, 0.14 on Reuters, WebKB, 20 Newsgroups, and Wine datasets, respectively

关键词： Clustering algorithms Accuracy Feature extraction Semantics Partitioning algorithms Particle swarm optimization Organizations Indexes Space exploration Particle measurements optimization algorithm feature extraction PSO text processing decision making

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Injection-mining scheme optimization of underground gas storage based on agent model

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2025年 244卷

作者： Yang, Huohai He, Qinghui Min, Chao Zhang, Ping Li, Hancheng Fu, Yu Li, Renze Li, Zonglin Duan, Chenglian Southwest Petr Univ Petr Engn Sch Chengdu Peoples R China Southwest Petr Univ Inst Artificial Intelligence Chengdu Peoples R China China Oil & Gas Pipeline Network Corp Beijing Peoples R China Sinopec Zhong Yuan Oilfield Branch Beijing Peoples R China

Underground natural gas storage (UNGS) plays a vital role in the peaking adjustment of gas supply. optimization of UNGS injection-production operations is essential for improving storage efficiency and capacity. This study first establishes and compares nine classical machine learning models such as physical neural network, gradient Boosting, and neural networks for prediction model preference, and finally selects extreme gradient Boosting (XG-Boost) to construct an agent model for predicting well pressure, which replaces the traditional complex mathematical model. Furthermore, a multi-objective optimization model was established after thea* optimization algorithm was preferred based on the agent model, with the objectives of maximizing the gas injection and extraction volume and minimizing the pressure difference between blocks by using a non-dominated sorting genetic algorithm-III (NSGA-III). We identified cases of injection-mining scheme optimization in the Wen23 large-scale UNGS and excluded substandard samples. By comparing the optimization results to the numerical model empirical scheme, the results indicate that the average differential pressure in each well zone decreased by 2% (33.3 MPa). Simultaneously, the maximum differential pressure between blocks was reduced significantly by 64%. On the other hand, after optimization at the end of gas production, the average well zone pressure increased by 6% (24.1 MPa), while the maximum differential pressure between blocks remained unchanged. This model also shows the merits of extreme conditions for injection mining, with the average value of the mean absolute error (MAE)between the optimized and numerical model schemes being 0.25. It is an effective optimization model that can assist and guide UNGS injection production to improve pressure distribution, gas diffusion range, and storage capacity.

关键词： Underground gas storage Injection-mining scheme Machine learning Agent model optimization algorithm

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