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ant colony algorithm and grey relation theory for manufacturing resources optimal allocation towards industry 5.0

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2025年

作者： Huang, Lang Zhuang, Mengzhen Tang, Hongtao Guo, Shunsheng Wei, Shupeng Zhang, Liang Lei, Deming Wuhan Univ Technol Sch Mech & Elect Engn Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Prov Engn Res Ctr Robot & Intelligent Mfg Wuhan Peoples R China Wuhan Univ Technol Sch Automat Wuhan Peoples R China

With the development of the building materials equipment manufacturing industry, due to the production process there are large-scale, small batch, multi-species, multi-subject characteristics, Manufacturing system design (MSD) of the building materials equipment manufacturing industry plays a key role in improving production efficiency. Compared with the traditional building materials equipment manufacturing industry, there are inefficiencies in production decision-making. In industry 5.0 advocates elastic manufacturing, by optimizing enterprise resource management for improving production efficiency and reducing costs. This paper uses a hybrid intelligent algorithm to solve the resource optimization problem, and successfully applies it in practical cases. By using fuzzy clustering, firstly, a multi-objective, multi-agent manufacturing equipment resource allocation model is established. Then, a solution based on grey correlation and an improved ant colony algorithm is presented, through the grey relational ranking, the manufacturing resources with high correlation degree are selected into the following optimal combination, and through the ant colony algorithm, the processing route on processing procedure of the order can be obtained. Finally, the effectiveness and feasibility of the proposed method are validated by experimental results, which show the optimized allocation system can significantly enhance production efficiency and reduce costs, for resource management of large-scale manufacturing systems for Industry 5.0.

关键词： ant colony algorithm grey correlation manufacturing resource allocation industry 5.0

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ant colony algorithm for Path Planning Based on Grid Feature Point Extraction

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 86-99页

作者：李二超齐款款 College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China

Aimed at the problems of a traditional ant colony algorithm,such as the path search direction and field of view,an inability to find the shortest path,a propensity toward deadlock and an unsmooth path,an ant colony algorithm for use in a new environment is ***,the feature points of an obstacle are extracted to preprocess the grid map environment,which can avoid entering a trap and solve the deadlock ***,these feature points are used as pathfinding access nodes to reduce the node access,with more moving directions to be selected,and the locations of the feature points to be selected determine the range of the pathfinding field of ***,based on the feature points,an unequal distribution of pheromones and a two-way parallel path search are used to improve the construction efficiency of the solution,an improved heuristic function is used to enhance the guiding role of the path search,and the pheromone volatilization coefficient is dynamically adjusted to avoid a premature convergence of the ***,a Bezier curve is used to smooth the shortest path ***,using grid maps with a different complexity and different scales,a simulation comparing the results of the proposed algorithm with those of traditional and other improved ant colony algorithms verifies its feasibility and superiority.

关键词： ant colony algorithm mobile robot path planning feature points Bezier curve grid map

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Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2025年第1期22卷

作者： Zhu, Yazhen Song, Qing Li, Meng Univ Jinan Sch Elect Engn 336 Nanxinzhuang West Rd Jinan 250022 Peoples R China

Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony algorithms and one strategic multi-AGV scheduling algorithm, respectively, thus verifying the effectiveness of the proposed method.

关键词： Multiple automated guided vehicles multitask scheduling ant colony algorithm prior time optimization

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Planning of a Single Flow Channel in Valve Blocks Based on Additive Manufacturing and the ant colony algorithm

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Chinese Journal of Mechanical Engineering 2024年第5期37卷 191-202页

作者： Jin Zhang Ziyang Li Yuying Zhang Yandong Liu Ying Li Xiangdong Kong School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Qinhuangdao 066004China National&Local Joint Engineering Research Center for Advanced Manufacturing Technology and Equipment Qinhuangdao 066004China State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou 310027China

As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks ***,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy ***,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive *** study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve *** layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline ***-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original *** fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.

关键词： Hydraulic valve block Flow channel B-spline curve Additive manufacturing ant colony algorithm

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ant colony algorithm Based on Total Logistics Delivery Efficiency Metrics for Crowdsourced On-Demand Delivery 14

Ant Colony Algorithm Based on Total Logistics Delivery Effic...

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14th Asian Control Conference (ASCC)

作者： Hou, Ying Qin, Xuemin Han, Honggui Huang, Yanting Du, Yongping Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9798331540845;9789887581598

Crowdsourced on-demand delivery is being required by many industries with the increasing demand of online shopping and on-demand food. However, it is difficult to ensure the delivery efficiency due to the uncertainty of delivery time for crowdsourced couriers in total delivery process. An ant colony algorithm based on total logistics delivery efficiency metrics is proposed in order to ensure the efficiency of crowdsourced on-demand delivery. Firstly, two metrics are designed to describe the total logistics delivery efficiency quantitatively. The average delivery time of each order and the number of orders completed on time are calculated in total logistics delivery efficiency metrics. Secondly, a total logistics crowdsourced on-demand delivery model (TLCODM) is built considering the transport efficiency and order punctuality. This model serves as the fundamental support for crowdsourced on-demand delivery. Finally, an ant colony algorithm based on total logistics delivery efficiency metrics (ACO-TLDE) is proposed to obtain the optimal scheduling solutions. The total logistics delivery efficiency metrics are used to guide the update of ant colony algorithm. The effectiveness of TLCODM and ACO-TLDE are verified by experiments.

关键词： ant colony algorithm total logistics delivery efficiency crowdsourced on-demand delivery

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GLOBAL PATH PLANNING METHOD FOR AGV OF WAREHOUSING LOGISTICS BASED ON IMPROVED ant colony algorithm

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2025年第1期32卷 159-172页

作者： Liu, Yan Henan Polytech Coll E Commerce & Logist Zhengzhou Peoples R China

AGV for warehousing and logistics is an automatic guided vehicle that is used for cargo handling, storage, sorting and other operations in warehousing and logistics scenarios. Due to the complex warehousing logistics scenarios, AGV needs to deal with the complex environment and variable task requirements in warehousing logistics during operation, resulting in low efficiency of path planning. Therefore, a global path planning method for AGV of warehousing logistics based on an improved ant colony algorithm is studied. After analyzing the overall transportation path of warehousing logistics, according to the optimization algorithm of ants' foraging behavior in nature, the pheromone transmission mechanism and behavior rules are simulated, and relevant factors such as path length transportation efficiency. Obstacle avoidance and load balance are considered to adjust the parameters such as pheromone volatilization factor and heuristic information weighting so that the improved ant colony algorithm can better adapt to changes in the warehousing logistics environment and improve the accuracy and reliability of AGV path planning. Through experimental verification, the effectiveness and superiority of the method are proved, the AGV transportation efficiency is improved, and the algorithm has excellent stability and adaptability.

关键词： Warehousing Logistics AGV Global Path Planning ant colony algorithm Improved ant colony algorithm Path Optimization

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Research on ant colony algorithm based on motion optimization and adaptive threshold for pantograph slider edge detection

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MEASUREMENT 2025年 245卷

作者： Yao, Xiaofeng Ma, Yuzhuo Dalian Jiaotong Univ Dalian Peoples R China

The pantograph slide is a part that directly contacts the catenary. Once the fault occurs, it will affect the flow quality of the locomotive and even cause major safety accidents such as scraping bows. Therefore, the online fault detection of pantograph slides is of great significance in ensuring the safe operation of railways. Accurate edge detection is necessary for carbon slide wear detection. Image-based fault diagnosis is an efficient method. Edge detection is a hot topic in image processing with many methods proposed. ant colony algorithms show promise for image edge detection. They are efficient at identifying edges in images. We propose an edge detection algorithm that combines edge detection technology with an adaptive ant colony optimization algorithm. First, the ant colony layout is optimized based on the gradient. The gradient value is used to determine the position of the ants. The ants are not randomly distributed but are placed on the highest gradient. Then, heuristic information is used in the construction stage. In the improvement stage, the movement probability of the ant colony is changed to optimize the movement path and obtain the optimal solution. Finally, the threshold adaptive algorithm is applied, which is expected to be used for better and faster path discovery optimization, and then the edge detection image is obtained according to the optimal solution. By comparing the traditional ant colony algorithm with other operators and edge detection algorithms, the proposed method has the greatest advantage over other methods in quantitative *** with traditional ACO algorithms, we have improved in PSNR, MSE, SSIM, and FSIM indexes, with a minimum improvement of 2.45% and a maximum improvement of 94.2%. The results show that the gradient-based position can improve the accuracy of edge detection, and the edge detection results are optimized by applying the adaptive ant colony algorithm.

关键词： ant colony algorithm Edge detection Fault detection Motion optimization Adaptive threshold

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GPS Phase Integer Ambiguity Resolution Based on Eliminating Coordinate Parameters and ant colony algorithm

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SENSORS 2025年第2期25卷 321-321页

作者： Liu, Ning Zhang, Shuangcheng Wu, Xiaoli Shen, Yu Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China Shaanxi Satellite Applicat Ctr Nat Resources Xian 710002 Peoples R China

Correctly fixing the integer ambiguity of GNSS is the key to realizing the application of GNSS high-precision positioning. When solving the float solution of ambiguity based on the double-difference model epoch by epoch, the common method for resolving the integer ambiguity needs to solve the coordinate parameter information, due to the influence of limited GNSS phase data observations. This type of method will lead to an increase in the ill-posedness of the double-difference solution equation, so that the fixed success rate of the integer ambiguity is not high. Therefore, a new integer ambiguity resolution method based on eliminating coordinate parameters and ant colony algorithm is proposed in this paper. The method eliminates the coordinate parameters in the observation equation using QR decomposition transformation, and only estimates the ambiguity parameters using the Kalman filter. On the basis that the Kalman filter will obtain the float solution of ambiguity, the decorrelation processing is carried out based on continuous Cholesky decomposition, and the optimal solution of integer ambiguity is searched using the ant colony algorithm. Two sets of static and dynamic GPS experimental data are used to verify the method and compared with conventional least squares and LAMBDA methods. The results show that the new method has good decorrelation effect, which can correctly and effectively realize the integer ambiguity resolution.

关键词： integer ambiguity QR decomposition transformation Kalman filter Cholesky decomposition ant colony algorithm decorrelation processing

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Path Planning of Agricultural Information Collection Robot Integrating ant colony algorithm and Particle Swarm algorithm

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IEEE ACCESS 2024年 12卷 50821-50833页

作者： Wu, Qiong Chen, Hua Liu, Baolong Xian Technol Univ Sch Comp Sci & Engn Xian 710021 Peoples R China Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

Faced with complex and ever-changing environmental conditions in the agricultural field, efficient agricultural information gathering is crucial for optimising agricultural output. Therefore, a new path planning algorithm combining ant colony algorithm and particle swarm optimization is proposed in this study. The aim is to achieve fast and accurate path planning for agricultural information gathering robots in diverse agricultural environments. The global search ability of particle swarm optimization algorithm in finding optimal paths and the local search advantage of ant colony algorithm in obstacle avoidance are used to optimise the movement strategy of robots in agricultural environments. The research results showed that the global path planning distance of this method was 19.328m. The execution time was 0.97s. In local path planning, the proposed algorithm had a fitness function value of 30.123 when the number of iterations reached 53. In mixed path planning, the proposed algorithm reduced the movement time by 3.2s. The conclusion shows that the algorithm proposed in this study has high applicability and efficiency in practical applications, providing an effective strategy for path planning of agricultural information gathering robots. It has important practical significance for promoting the development of intelligent agriculture.

关键词： ant colony algorithm particle swarm optimization algorithm agriculture robot path pheromone

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Massive Music Resources Retrieval Method Based on ant colony algorithm

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第5期18卷 1208-1222页

作者： Meng, Yun Commun Univ Shanxi Performance Sch Jinzhong 030619 Peoples R China

Music resources are characterized by quantization, diversification and complication. With the rapid increase of the demand for music resources, the storage of music resources is very large. In order to improve the retrieval effect of music resources, a massive music resources retrieval method based on ant colony algorithm is proposed to effectively use music resources. This paper constructs autocorrelation function to extract pitch feature of music resource, classifies the music resource information by calculating feature similarity. Using ant colony algorithm to correlate the feature of music resource, gain the result of correlative, locate the result of detection and get the result of multi-module. Simulation results show that the proposed method has high precision and recall, short retrieval time and can effectively retrieve massive music resources.

关键词： ant colony algorithm music resource autocorrelation function feature similarity information gain

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