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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Du, Xikang Shenyang Aerospace University College of Mechanical and Electrical Engineering Liaoning Shenyang23456 China

ISBN: (纸本)9798350308167

Path planning technology is the core part of UAV field, the purpose of path planning is to provide a feasible and safe optimal path for UAV flight, to meet the feasibility of UAV operation, and improve the path optimization ability of UAV in complex and multi-obstacle environment. Traditional path planning algorithms have low computational efficiency and are prone to local extremes. In this paper, it is proposed that the three-dimensional path planning of UAV based on ant colony algorithm can improve the effectiveness of UAV dynamic constraint processing and enhance the path optimization ability in multi-obstacle environment. The advantages of this algorithm are fast convergence speed, simple calculation process, and few parameters, etc., and it has become the first choice for UAV planning three-dimensional path. At the same time, a multi-obstacle model is constructed in a three-dimensional environment, and the fitness function is constructed on the obstacle threat, path length and navigation height integration. After dynamic 3D environment simulation experiments, the results show that the ant colony algorithm can significantly improve the UAV path optimization ability and ensure the stability of 3D path planning. © 2023 IEEE.

关键词： 3D paths ant colony algorithm Path planning UAVs

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Optimization Model of Housing Construction Project Scheduling Based on Optimized ant colony algorithm 2

Optimization Model of Housing Construction Project Schedulin...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasa, Gowda G.K. Krishna, G.R. Sanjay Rex, J. Kumar, K. Sambath Kumar, Madhan Mohan Manikandan, N. Department of Computer Science and Engineering ACS College of Engineering Tamilnadu Bangalore India Department: Mechanical Engineering Koneru Lakshmaiah Education foundation Green fields Andhra Pradesh Vaddeswaram India Department of Civil Engineering Malla Reddy Engineering College Tamilnadu Hyderabad India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Robotics and Automation Easwari Engineering College Bharathi salai Ramapuram Tamilnadu Chennai India Dept of Mechanical Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798350305418

Construction management is a standardized means of project management. The construction project takes a long time and has many links, and the construction workers involved are complicated. Poor quality buildings will not only affect the construction enterprises’ brand, but also threaten the safety of people's lives and property. In this paper, an optimization model of housing construction project scheduling based on ant colony algorithm (ACA) is put forward. By studying the relationship among various sub-projects in the construction system of building projects, the management of building projects can be better realized. The results show that compared with the other two algorithms, the optimized ACA has certain advantages in recall and accuracy. There are many contents involved in the building construction process, and there are many uncontrollable factors in the building process, which greatly impact the project's construction quality. The construction industry can use this technology to strengthen the building construction management, solve the problems existing in the construction management, and improve the construction quality. © 2023 IEEE.

关键词： ant colony algorithm Building engineering Construction supervision Optimization Model Project scheduling

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Research on Patrol Path Optimization Based on Improved ant colony algorithm 4th

Research on Patrol Path Optimization Based on Improved Ant C...

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4th International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Zhou, Haihui Shanghai Armed Police Corps Shanghai China

ISBN: (纸本)9789819653171

Patrol path optimization is of great significance in various real applications. At present, there are many ways to optimize the patrol path from various angles, and there are also many studies on traditional ant algorithms. After studying the application of the existing traditional ant colony algorithm in patrol path optimization, we analyze the existing problems and difficulties, and propose an improved ant colony algorithm. After deeply studying the ant colony algorithm and patrol path optimization problem, the paper tries to introduce the improved ant colony algorithm into the problem based on the modeling of the patrol path problem. The experimental results show that the patrol path optimization method based on the improved ant colony algorithm has certain advantages over the ordinary patrol path optimization method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： ant colony algorithm Improvement Optimization Model Patrol Path

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Research on path planning of electric tractor based on improved ant colony algorithm 25

Research on path planning of electric tractor based on impro...

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25th International Conference on Electrical Machines and Systems (ICEMS)

作者： Liang Chuandong Lu Min Shihezi Univ Coll Mech & Elect Engn Shihezi Peoples R China

ISBN: (纸本)9781665493024

The emergence of electric tractors and related control technologies has accelerated the development of modern agriculture, but the path planning problem of electric tractors affects their operating range to a certain extent. In this paper, an improved ant colony algorithm is proposed to address the problems that the basic ant colony algorithm in the path planning of electric tractors is prone to local optimal solutions and slow convergence speed. Based on the idea of "Newton's cooling law", the pheromone volatility factor and state transfer probability function are improved to enhance the ability of global search in the early iteration of the algorithm and accelerate the convergence speed in the middle and late iteration;the early termination strategy of the algorithm iteration is introduced to reduce the iteration redundancy and shorten the running time of the algorithm;based on the kinematic model of the electric tractor, a mathematical model of energy loss is established to shorten the running time of the algorithm. Based on the kinematic model of electric tractor, the mathematical model of energy loss is established, and the evaluation index of the optimal path is established. The simulation results show that compared with the literature algorithm and the basic ant colony algorithm, the energy loss of the electric tractor is reduced by 18.31% and 28.96%, the optimal path length is shortened by 0.81% and 0.97%, and the running time is reduced by 20.13% and 18.43%, respectively. The comprehensive performance of the improved algorithm in this paper is excellent, which verifies the optimization effect.

关键词： Path planning Electric tractors ant colony algorithm Pheromone volatility factor State transfer probability function

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Routing networking technology based on improved ant colony algorithm in space-air-ground integrated network

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 34页

作者： Nie, Wuzhou Chen, Yong Wang, Yuhao Wang, Peizheng Li, Meng Ning, Lei Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

Space-air-ground integrated networks comprise a multi-level heterogeneous integrated network that combines satellite-based, aerial, and terrestrial networks. With the increasing human exploration of space and growing demands for internet applications, space-air-ground integrated networks have gradually emerged as the direction for communication network development. These networks face various challenges such as extensive coverage, diverse communication node types, low-quality communication links, and simultaneous operation of multiple network protocols. However, the rapid development and widespread application of artificial intelligence and machine learning technologies in recent years have offered new perspectives and solutions for the communication architecture and routing algorithm research within space-air-ground integrated networks. In these networks, not all nodes can typically communicate directly with satellites;instead, a specific set of specialized communication nodes facilitates data communication between aerial and satellite networks due to their superior communication capabilities. Consequently, in contrast to traditional communication architectures, space-air-ground integrated networks, particularly in the terrestrial layer, often need to address challenges related to the diversity of communication node types and low-quality communication links. A well-designed routing approach becomes crucial in addressing these issues. Therefore, this paper proposes an AODV routing network protocol based on an improved ant colony algorithm (AC-AODV), specifically designed for the terrestrial layer within the space-air-ground integrated networks. By integrating information such as the type, energy, and location of communication nodes, this protocol aims to facilitate network communication. The objective is to guide information flow through nodes that are more suitable for communication, either by relaying communication or by connecting with satellites through speciali

关键词： Space-air-ground integration Terrestrial layer network ant colony algorithm Routing networking protocol

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Optimization of Integrated Circuit Routing Based on ant colony algorithm

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Procedia Computer Science 2025年 262卷 1367-1373页

作者： Yiming Chen Yuhan Feng School of Physics and Electromechanical Engineering Hubei University of Education Wuhan 430205 China James Watt Engineering College University of Glasgow Glasgow Scotland UK

In order to solve the complexity and NP-hard problem of Integrated circuit (IC) routing, this paper presents an optimization method of IC routing based on ant colony algorithm. This method combines the advantages of ant colony algorithm in solving complex combinatorial optimization problems. By simulating the pheromone renewal and route selection mechanism of ant colony in the process of searching for food, the algorithm in this study can intelligently explore the wiring space and find the optimal or near-optimal routing scheme. Experimental results show that compared with traditional algorithms, this method can significantly improve the routing efficiency, reduce the routing length, and ensure the quality of the routing. It can provide a new and effective optimization method for IC design.

关键词： Integrated circuit ant colony algorithm Routing optimization

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Path Planning for Mobile Robots Based on Improved ant colony algorithm 6th

Path Planning for Mobile Robots Based on Improved Ant Colony...

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6th International Conference on Cognitive Computing (ICCC) Held as Part of the Services Conference Federation (SCF)

作者： Zhang, Jie Pan, Xiuqin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

ISBN: (纸本)9783031235849;9783031235856

In a two-dimensional environment, the traditional ant colony algorithm path planning is prone to problems, such as many turning points, easily falling into a local minimum, and the path is not smooth. To address these problems, a new improved ant colony algorithm is proposed to improve the path optimization performance. First, according to the position of the current grid relative to the start point and the end point, a non-uniform initial pheromone strategy is proposed, so that the closer the dominant grid is, the higher the pheromone concentration is, avoiding blind search by ants and reducing invalid search, and then the introduction of an angular guidance factor to increase the guidance to the end point and to avoid the probability of path zigzagging due to small differences in adjacent grid pheromones, next the pheromone update strategy with a reward and punishment mechanism, the enhancement and decay factors are introduced to adjust the pheromone values adaptively to improve the convergence of the algorithm, final the improved ant colony algorithm and genetic algorithm are fused, and the path is smoothed using the piecewise B-spline curve strategy. The experimental results show that the improved algorithm has greatly improved both the optimization finding ability and the convergence ability.

关键词： Path planning ant colony algorithm Non-uniform pheromone Angular guidance factor Reward and punishment mechanism Enhancement factors Decay factors Genetic algorithm Piecewise B-spline curve

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Monte Carlo calculation of the photon beam quality correction factor kQ, Q0 for ionization chambers of very small volume: Use of variance reduction techniques driven with an ant colony algorithm

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RADIATION PHYSICS AND CHEMISTRY 2024年 225卷

作者： Garvi, A. M. Zamora, L. I. Anguiano, M. Lallena, A. M. Garcia-Pareja, S. Univ Granada Dept Fis Atom Mol & Nucl E-18071 Granada Spain Hosp Reg Univ Virgen Nieves Serv Fis & Protecc Radiol E-18014 Granada Spain Univ Granada Complejo Hosp Univ Granada Inst Invest Biosanit Ibs GRANADA E-18016 Granada Spain Hosp Univ Reg Malaga UGC Radiofis Hosp Avda Carlos Haya S-N E-29010 Malaga Spain

Purpose : To evaluate the effectiveness of an ant colony algorithm in implementing variance reduction techniques in the Monte Carlo computation of the photon beam quality correction factor kQ , Q 0 for ionization chambers characterized with very small active volumes. Methods: The Monte Carlo code PENELOPE has been utilized to compute the photon beam quality correction factor kQ , Q 0 for the Semiflex 3D 31021 ionization chamber, which has an active volume of 0.071 . 071 cm3. 3 . Various clinical photon beams generated with nominal potentials from 4 to 25 MV have been considered, with a 60 Co beam serving as the reference. The calculation involved determining the absorbed dose to both water and the sensitive volume of the ionization chamber. This information was used to derive the f c,Q , Q factors for the photon beams and the f c,Q0 , Q 0 factor for the 60 Co beam, whose ratio provides the k Q , Q 0 factors. Results: The algorithm has been initially validated by comparing with analog simulations where no variance reduction techniques are applied. The results have demonstrated an efficiency improvement ranging from a factor of 7 to 44. By incorporating the ant colony algorithm along with the variance reduction techniques, the determination of TPR 20 10 values for various studied photon beams has been achieved. The calculated k Q , Q 0 factors agree with previously published values. Two distinct protocols outlined in the TRS-398 have been taken into account and the results obtained for these protocols were compared to explore any differences between them. Conclusions: The ant colony algorithm facilitates the automatic application of variance reduction techniques, such as splitting and Russian roulette, without the need to delve into the geometric intricacies of the simulation. This automated approach results in increased efficiency, enabling simulations to be conducted within reasonable times while maintaining uncertainties at levels that ensure reliability.

关键词： ant colony algorithm Splitting Russian roulette Monte Carlo PENELOPE Ionization chamber Beam quality correction factor

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Study on Route Optimization of Shenyang subway Network Base on Improved ant colony algorithm 34

Study on Route Optimization of Shenyang subway Network Base ...

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34th Chinese Control and Decision Conference (CCDC)

作者： Jin Zhongzhen Shao Qingliang Luan Mengyu Zhangzhao Zhao Xiaoyu Song Qian Shenyang Aerosp Univ Elect & Informat Engn Shenyang 110136 Peoples R China

ISBN: (纸本)9781665478960

Shenyang underground route planning algorithm is the shortest route planning algorithm based on ant colony algorithm (ACA). ACA has a leading position in routing and route planning with a great space for development. In view of the defects of long random search time, premature stagnation in the basic ACA, in order to facilitate the subsequent improvement of route planning in other provinces and cities, we adjusted the ACA adaptively. Route planning algorithm has stronger adaptability and machinability after adjusted. This improved ACA include three planning programming: point to point, point to plane, and Planar programming. It add the speed and accuracy of planning and reduce The calculation amount of the algorithm.

关键词： ant colony algorithm route planning python pheromone

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Optimization of Material Distribution Route Based on Green Logistics and ant colony algorithm 6

Optimization of Material Distribution Route Based on Green L...

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6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Xu, Tiantian Chen, TianMei Zhang, Ying Yu, ChenXi State Grid Zhejiang Producement Co Enterprise Supply Chain Operat Ctr 378 Fengqi Ave Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781665454889

Aiming at the problem of slow development of electric logistics due to the low efficiency of traditional electric materials distribution, an optimization method of material distribution route based on ant colony algorithm was proposed. First of all, the multi-objective function was constructed based on customer satisfaction and the shortest distribution distance. Then, combined with the disadvantages of the traditional ant colony algorithm, the pheromone, volatile factor and heuristic factor of the ant colony algorithm were improved to solve the multi-objective function. Finally, the effectiveness of the algorithm was verified by simulation test. Simulation results showed that compared with traditional ant colony algorithm, when the number of terminal distribution points is 20, the shortest distribution distance obtained by the optimized ant colony algorithm is reduced by 1.7%, and the convergence speed of the algorithm is improved by 34.5%. Therefore, the optimized ant colony algorithm is better than the traditional ant colony algorithm, which can improve the distribution efficiency of electric power material.

关键词： ant colony algorithm multi-objective function route optimization green logistics

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