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31st International Conference on Computers in Education (ICCE)

作者： Zheng, Yaqian Wang, Deliang Xu, Yaping Mao, Ziqi Zhao, Yaqi Li, Yanyan Beijing Normal Univ Sch Educ Technol Beijing Peoples R China Beijing Normal Univ Res Ctr Knowledge Engn Beijing Peoples R China Univ Hong Kong Fac Educ Hong Kong Peoples R China

ISBN: (纸本)9786269689019

The recommendation of personalized learning paths is recognized as one of the most challenging aspects in the field of e-learning. In the existing literature, numerous approaches have been proposed to identify appropriate learning paths for e-learners, taking into consideration multiple perspectives. However, the current state of research lacks a unified framework that effectively integrates the most vital parameters associated with the learner, learning object (LO), and domain knowledge to generate optimal learning paths. To address this challenge, a novel bio-inspired approach is proposed for solving the personalized learning path problem. In this method, we initially incorporate the learner, LO and domain knowledge models into a unified mathematical model. Then an enhanced ant colony optimization algorithm is utilized to determine the optimal personalized learning paths for learners. To investigate the effectiveness of the proposed method, we performed several computational experiments based on six simulation datasets. The results indicate that the proposed method surpasses other competing methods in terms of performance and robustness, showcasing its superior effectiveness.

关键词： E-learning learning path recommendation unified model ant colony optimization algorithm

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Modeling and performance evaluation of wind turbine based on ant colony optimization-extreme learning machine

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APPLIED SOFT COMPUTING 2020年 94卷 106476-106476页

作者： Wen, Xiaoqiang Northeast Elect Power Univ Dept Automat Jilin Jilin Peoples R China Jilin Prov Int Res Ctr Precis Drive & Intelligent Jilin Jilin Peoples R China

In this paper, an innovative hybrid multi-variable generator's actual-output-power predicting model is proposed based on ant colony optimization algorithm and extreme learning machine network, and a data-driven performance evaluation model is presented based on the two indices, K-means clustering algorithm and Markov chain for the performance evaluation of the wind turbines. ant colony optimization algorithm is used to optimize the initial weights and thresholds of the extreme learning machine network, then the optimized combinations of weights and thresholds are provided into the extreme learning machine models to overcome the sensitivity problem of initialization setting and the disadvantage of easily falling into local optimum. Through the actual-output-power prediction of the WTs in a wind farm, the results show that the proposed model has more higher prediction accuracy than other methods mentioned in this paper. The optimization process also shows that the prediction accuracy is sensitive to the number of hidden-layer nodes and is relatively insensitive to other model parameters. Then, the data-driven performance evaluation models are proposed based on the error sequences obtained above. The case study is conducted and the results show that the method can evaluate the operating performance of the wind turbines correctly. The effectiveness of the evaluation results is also verified by the actual operation results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： ant colony optimization algorithm Extreme learning machine network Generator's actual output power Performance evaluation Wind turbine

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Development and Control of an upper Limb Rehabilitation Robot via ant colony optimization-PID and Fuzzy-PID Controllers

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INTERNATIONAL JOURNAL OF ENGINEERING 2022年第8期35卷

作者： Mirrashid, N. Alibeiki, E. Rakhtala, S. M. Islamic Azad Univ Dept Elect Engn Aliabad Katoul Branch Aliabad Katoul Iran Golestan Univ Fac Engn Dept Elect Engn Gorgan Golestan Iran

The control of movement rehabilitation robots is necessary for the recovery of physically disabled patients and is an interesting open problem. This paper presents a mathematical model of the upper limb rehabilitation robot using Euler-Lagrange approach. Since the PID controller is one of the most popular feedback controllers in the control strategy due to its simplicity, we an ACO-PID controller for an upper limb rehabilitation robot. The main part of designing the PID controller is determining the gains of the controller. For this purpose. we ant colony optimization algorithm (ACO) to tune the coefficients. To evaluate the validity of the proposed controller, we have compared it to Fuzzy-PID controller and the PID controller adjusted with the Ziegler-Nichols method (ZN-PID). The results that the performance of the ACO-PID controller is better than the others. Also, the adaptive PID controllers (ACO-PID and Fuzzy-PID) ensure accurate tracking, finite-time convergence, and stability. The results that the mean absolute error and normalized root mean square (NRMS) of tracking error using the CO-PID are less than that using the Fuzzy-PID and ZN-PID controller.

关键词： Rehabilitation Robot ant colony optimization algorithm Fuzzy-PID Controller Ziegler-Nichols Method PID Tuning

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System Management Model Based on ABC-ACO algorithm under the Background of Digital Economy 1

System Management Model Based on ABC-ACO Algorithm under the...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Fu, Yong College of Economics Sichuan Agricultural University Sichuan Chengdu China

ISBN: (纸本)9798350316469

With the rapid development of the digital economy, the need to manage complex systems is increasing day by day, and traditional management methods are often unable to fully adapt to this challenge. Therefore, this research proposes a system management model based on the ABC-ACO algorithm, which aims to optimize decision-making and resource allocation and improve system performance and efficiency. In this paper, the ABC-ACO algorithm is used as the main optimization tool. The algorithm combines artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO), and has the characteristics of adaptive search and iterative optimization. Through experimental tests, the accuracy rate of the algorithm in this paper is 92% at the highest and 86% at the lowest. The system management model based on the ABC- ACO algorithm performs well in the context of the digital economy. By optimizing decision-making and resource allocation, the model can improve the performance and efficiency of the system. © 2023 IEEE.

关键词： ABC-AOC algorithm ant colony optimization algorithm Artificial Bee colony algorithm Digital Economy

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ant colony optimization Task Scheduling algorithm for SWIM Based on Load Balancing

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FUTURE INTERNET 2019年第4期11卷 90-90页

作者： Li, Gang Wu, Zhijun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Baicheng Normal Univ Sch Mech Engn Baicheng 137000 Peoples R China Civil Aviat Univ China Sch Elect Informat & Automat Tianjin 300300 Peoples R China

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.

关键词： System Wide Information Management ant colony optimization algorithm hardware performance quality index load standard deviation function load balancing

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Research on satellite laser ranging observation task scheduling method

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第4期35卷 046002-046002页

作者： Lv, Zequn An, Ning Fan, Cunbo Liu, Chengzhi Li, Zhenwei Gao, Jian Wen, Guanyu Dong, Xue Chinese Acad Sci Changchun Observ Natl Astron Observ Changchun 130117 Peoples R China Univ Chinese Acad Sci Shenyang 100049 Peoples R China Chinese Acad Sci Changchun Branch Changchun 130117 Peoples R China

Satellite laser ranging (SLR) is a technology with the highest precision of single measurement of satellite radial distance, which is developing rapidly in the direction of long-distance, high-precision and automation. SLR autonomous observation task scheduling is an important step in realizing station automation, which needs to satisfy the principles of satellite tracking priority and maximization of observation revenue at the same time. In order to improve the automation and intelligence level of SLR system, based on the framework of ant colony optimization (ACO) algorithm, this paper combines the dynamic optimization characteristics of ACO algorithm and the local optimization characteristics of greedy algorithm, introduces the maximum-minimum ant mechanism, and puts forward a scheduling scheme for SLR observation task based on greedy ant colony algorithm (GACA). The results show that compared to the current scheduling methods applied in practice. The results show that compared with the current scheduling method applied in practice, the number of observation satellites obtained from the GACA algorithm-based observation task planning for the SLR system has been improved by 37.4%, the total arc segment of satellite observation with higher priority has been extended by 36.47%, and the total observation gain has been increased by 42.39% in the same period of time. It effectively solves the problems of low efficiency, easy to miss stars and less stars in the observation process in manual scheduling, and provides a simple, practical, efficient and convenient observation task planning scheme for the establishment of an unmanned SLR system.

关键词： satellite laser ranging observation task scheduling ant colony optimization algorithm automated ranging system

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An Efficient Scheduling and Navigation Approach for Warehouse Multi-Mobile Robots 13th

An Efficient Scheduling and Navigation Approach for Warehous...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Zhao, Kang Xu, Benlian Lu, Mingli Shi, Jian Li, Zhen Soochow Univ Sch Mech & Elect Engn Suzhou Peoples R China Changshu Inst Technol Sch Mech Engn Changshu Jiangsu Peoples R China Changshu Inst Technol Sch Elect & Automat Engn Changshu Jiangsu Peoples R China

ISBN: (纸本)9783031097263;9783031097263

Multi-robot scheduling and navigation methods are critical for efficient warehouse handling. In this paper, we propose a Robot Operating System (ROS) based scheduling and navigation method for multi-mobile robots. In order to solve the problem of multi-robot multi-task point assignment in the warehouse environment, we establish a target model that minimizes the total transportation time and propose a hierarchical Genetic algorithm-ant colony optimization algorithm. By repeating the upper and lower operations, the shortest total transport time allocation scheme for multi-robot multi-tasking can be obtained. In order to realize the multi-robot path planning after task assignment, a multi-robot communication system is designed on the basis of ROS, and the autonomous navigation of mobile robots is employed with the help of SLAM map. The experimental results show that the proposed multi-robot scheduling method can effectively reduce the overall transportation time, realize the reasonable allocation of multi-robots and multi-tasks, and successfully complete the cargo transportation task.

关键词： Multi-robot scheduling Genetic algorithm ant colony optimization algorithm Navigation SLAM

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Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第4期18卷 903-921页

作者： Zhou, Fengjiao Ma, Rui Samsurijan, Mohamad Shaharudin bin Xie, Xiaoqin Univ Sains Malaysia Sch Social Sci George Town 11800 Gelugor Malaysia Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu 611137 Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Publ Adm Chengdu Sichuan Peoples R China

Industry 5.0 puts forward higher requirements for smart cities, including low -carbon, sustainable, and people -oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

关键词： Smart City Cyber-Physical-Social Systems Smart Patrol Platform Drone Trajectory Planning ant colony optimization algorithm

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Path Planning for Autonomous Underwater Vehicles Under the Influence of Ocean Currents Based on a Fusion Heuristic algorithm

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第9期70卷 8529-8544页

作者： Wen, Jiabao Yang, Jiachen Wang, Tianying Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents.

关键词： Heuristic algorithms Path planning Oceans Convergence Clustering algorithms Genetic algorithms Simulated annealing Genetic algorithm ant colony optimization algorithm autonomous underwater vehicle path planning heuristic algorithms fusion

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Meta-Heuristic algorithms for Learning Path Recommender at MOOC

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IEEE ACCESS 2021年 9卷 59093-59107页

作者： Son, Ngo Tung Jaafar, Jafreezal Aziz, Izzatdin Abdul Anh, Bui Ngoc Univ Teknol Petronas Dept Comp & Informat Sci Seri Iskandar 32610 Perak Malaysia FPT Univ Informat & Commun Technol Dept Hanoi 100000 Vietnam

Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic algorithm (GA) and ant colony optimization algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera's courses and Vietnam work's jobs. The test results show the effectiveness of the proposed method.

关键词： optimization Electronic learning Data mining Genetic algorithms Computer aided instruction Java ant colony optimization Learning path Knowledge-based recommendation Knowledge graph multi-objective optimization compromise programming genetic algorithm ant colony optimization algorithm

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