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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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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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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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An Automatic Route Planning Method for Digital Transmission Lines Based on Remote Sensing Spatial Model of Ground Obstacles and ant colony algorithm

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年 1-14页

作者： Wan, Guoping Han, Xiping Zhao, Xing Liu, Meng Beijing Zhengneng Space Informat Technol Co Ltd Beijing 102600 Peoples R China

Transmission lines often need to cross various complex geographical environments, such as mountainous areas, rivers, forests, etc., where the terrain and landforms are complex and varied. In order to address the challenges faced by transmission line path planning and solve the problems of traditional planning methods relying on manual experience, outdated maps, and existing research methods such as the inability to completely avoid obstacles, high planning costs, and susceptibility to dimensional disasters, a digital transmission line automatic path planning method based on ground obstacle remote sensing spatial model and ant colony algorithm is proposed. Using unmanned aerial vehicles equipped with remote sensing sensors to collect ground data, extracting key features through preprocessing, constructing a ground obstacle spatial model, and estimating construction costs. On this basis, combined with an optimized ant colony algorithm, automatic planning of transmission line paths based on cost-benefit analysis was achieved, and a detailed planning cost map was drawn. Experiments have shown that this method can effectively avoid impassable areas in transmission lines, remove unnecessary corners in feasible areas, and plan construction with low difficulty and cost. The planning accuracy is higher than 98%, and the performance is good.

关键词： Ground obstacle Spatial model ant colony algorithm Transmission line Raster matrix Path planning

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ant colony algorithm for the Multi-Depot Vehicle Routing Problem in Large Quantities by a Heterogeneous Fleet of Vehicles

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INFOR 2013年第1期51卷 31-40页

作者： Benslimane, Mohammed Taha Benadada, Youssef Univ Mohammed V Souissi ENSIAS Operat Res & Logist Team ROL Rabat Morocco

This article describes a heuristic method based on an ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles. Test results on different problem instan... 详细信息

关键词： distribution products in large quantities vehicle routing problem ant colony algorithm

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ant colony algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud

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IEEE ACCESS 2019年 7卷 83088-83100页

作者： Lin, Miao Xi, Jianqing Bai, Weihua Wu, Jiayin South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Zhaoqing Univ Sch Comp Sci Zhaoqing 526061 Peoples R China Guangdong Vocat Coll Post & Telecom Sch Comp Guangzhou 510630 Guangdong Peoples R China

In cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of solutions and implementations, there remain open issues that have not been completely addressed in the deployment and management of the microservice containers. An effective method for container resource scheduling not only satisfies the service requirements of users but also reduces the running overhead and ensures the performance of the cluster. In this paper, a multi-objective optimization model for the container-based microservice scheduling is established, and an ant colony algorithm is proposed to solve the scheduling problem. Our algorithm considers not only the utilization of computing and storage resources of the physical nodes but also the number of microservice requests and the failure rate of the physical nodes. Our algorithm uses the quality evaluation function of the feasible solutions to ensure the validity of pheromone updating and combines multi-objective heuristic information to improve the selection probability of the optimal path. By comparing with other related algorithms, the experimental results show that the proposed optimization algorithm achieves better results in the optimization of cluster service reliability, cluster load balancing, and network transmission overhead.

关键词： ant colony algorithm cloud computing container scheduling microservices multi-objective optimization

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Optimization deployment of wireless sensor networks based on culture-ant colony algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2015年 250卷 58-70页

作者： Sun, Xuemei Zhang, Yiming Ren, Xu Chen, Ke Tianjin Polytech Univ Sch Comp Sci & Software Tianjin 300382 Peoples R China

The optimization of nodes deployment is one of the most active research areas in wireless sensor networks. In this paper, we propose an improved culture algorithm-ant colony algorithm (CA-ACA) to solve the problem of nodes deployment. Double evolution mechanism of culture algorithm is integrated into the improved ant colony optimization algorithm within the population space as an evolutionary strategy, and then directs the search of population space through the elites of continuous evolution in belief space. The introduction of culture algorithm makes the search for optimization faster and better stability of CA-ACA than traditional ones. In addition, greedy strategy is introduced for the situation of sparsely monitored points, which makes CA-ACA be suitable for any environment. Furthermore, we also investigate the convergence judging method which makes CA-ACA avoid premature convergence so as to achieve the purpose of global optimization. A large number of simulation experiments have been conducted and the results not only demonstrate the validity of CA-ACA, but also verify that CA-ACA algorithm can optimize the number of sensors deployed in network under the conditions of guaranteed connectivity and coverage. Current results are of great significance to effectively design the optimal deployment of nodes in wireless and mobile sensor networks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Wireless sensor networks Nodes deployment Coverage Connectivity Culture algorithm ant colony algorithm

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ant colony algorithm based on magnetic neighborhood and filtering recommendation

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SOFT COMPUTING 2021年第13期25卷 8035-8050页

作者： Yu, Jin You, Xiaoming Liu, Sheng Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Univ Engn Sci Sch Management Shanghai 201620 Peoples R China

To deal with the problems that the ant colony algorithm has slow convergence speed and easy falling into the local optimum when solving TSP, an ant colony algorithm based on magnetic neighborhood and filtering recommendation (MRACS) is proposed to solve these problems. First, a dynamic magnetic neighborhood strategy is adopted to balance the convergence speed and the solution accuracy by magnetic attraction. It attracts ants to enlarge the exploration of a better neighborhood, thus improving the accuracy of the result. Second, a cross-excitation strategy based on filtering recommendation is applied to increase the diversity of the algorithm by dynamic weakening or enhancing local pheromones in the neighborhoods. It aids the algorithm get rid of the local optimum. Through the simulation experiments and the rank-sum test analysis, it is observed that the MRACS can effectively balance the convergence speed and the accuracy of the solution.

关键词： ant colony algorithm TSP Collaborative filtering recommendation Magnetic neighborhood

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