检索结果-内蒙古大学图书馆

15th International Symposium on Electromagnetic Launch (EML) Technology

作者： Liu, Wenbiao Cao Yanjie Zhang, Yuan Wang, Jie Yang, Dawei Naval Aeronaut & Astronaut Univ Yantai 264001 Shandong Peoples R China

Electrical-to-kinetic energy conversion efficiency of synchronous induction coilguns (SICG) is the main limiting factor of its development. In the system of SICG, the change of any electromagnetic parameters can directly or indirectly affects the electrical-to-kinetic energy conversion efficiency. In order to improve the electrical-to-kinetic energy conversion efficiency of SICG, the electromechanical model of SICG was built at first in this paper. Then, the structural parameters of SICG with 60-mm caliber were optimized with ant colony optimization algorithm. The electrical-to-kinetic energy conversion efficiency of the system was regarded as the target function, while the center-to-center space between the drive coil and the armature, and the structural parameters of the drive coil and the armature were regarded as variables. Results of research indicated that the electrical-to-kinetic energy conversion efficiency of the system was improved through parameter optimization. In order to validate the results of parameter optimization, an experiment was carried out with the first stage of SICG.

关键词： ant colony optimization algorithm armature drive coil energy conversion efficiency efficiency of synchronous induction coilguns (SICG)

来源：评论

学校读者我要写书评

暂无评论

The dynamics of ant colony optimization algorithms applied to binary chains

引用

SWARM INTELLIGENCE 2012年第4期6卷 343-377页

作者： Iacopino, Claudio Palmer, Phil Univ Surrey Surrey Space Ctr Guildford GU2 7XH Surrey England

In the last decade ant colony optimization (ACO) algorithms have received increasing attention due to their flexibility and adaptability to different applications. Despite these advantages, their design and analysis are still critical issues;research on formal methods could increase the reliability of these systems and extend their applications to critical scenarios such as space or military. This paper aims at exploring the potential of formal modelling techniques already developed for studying dynamical systems. The benefits of these techniques are shown in the analysis of a generic ACO algorithm applied to problems modelled as binary chains. The theoretical model developed is able to give new insights on the overall system dynamics, predicting the system long-term behaviours and the influence of specific parameters on such behaviours. This paper first offers a complete stability analysis for a basic problem providing an easy description of the key concepts before generalizing the model to problems of n nodes, allowing its application to real problems. The picture of the system dynamics is then completed by a convergence time analysis, which is necessary to draw sensible conclusions.

关键词： ant colony optimization algorithm Change of stability Dynamical systems ODE Equilibrium point Parameter impact analysis

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Nuclear Radiation Environment Path Planning Method Based on an Improved ant colony algorithm

引用

NUCLEAR TECHNOLOGY 2025年

作者： Xiang, Suo-Yi Zhou, Huai-Fang Huo, Jian-Wen Zhang, Hua Gu, Chao-Fan Southwest Univ Sci & Technol Sch Informat & Engn Mianyang 621010 Sichuan Peoples R China Southwest Univ Sci & Technol Sichuan Key Lab Special Environm Robot Mianyang 621010 Sichuan Peoples R China State Grid Guangyuan Power Supply Co Guangyuan 628000 Peoples R China

Minimum cumulative dose path planning is an important radiation protection measure to reduce the radiation exposure of robots in nuclear emergencies. However, when an emergency or accident occurs, the distribution of radiation doses in the environment changes dynamically, making the cumulative radiation dose of paths planned by traditional methods nonoptimal. This study proposes a Dijkstra-improved ant colony optimization algorithm (DIACO) to address this issue, combined with a segmented search method to achieve path planning in a dynamic radiation *** method transforms the minimal cumulative radiation dose path obtained by the Dijkstra algorithm into an increment of the initial pheromone distribution for the ant colony optimization (ACO) algorithm, improves the heuristic factor of the ACO algorithm, and incorporates the maximum-minimum ant system to enhance the algorithm's convergence *** results show that the proposed DIACO algorithm reduces the cumulative radiation dose of the obtained path by approximately 21.08%, the travel distance to the target by about 33.87%, and the number of turns by about 85.1% compared to the traditional ACO algorithm.

关键词： Minimum cumulative dose path planning radiation exposure radiation protection ant colony optimization algorithm dynamic radiation environment

来源：评论

学校读者我要写书评

暂无评论

Joint observation and transmission scheduling of multiple agile satellites with energy constraint using improved ACO algorithm

引用

ACTA ASTRONAUTICA 2025年 230卷 92-103页

作者： Shang, Minghao Yuan, Ronggang Song, Bin Huang, Xuxing Yang, Bin Li, Shuang Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Shanghai Inst Aerosp Syst Engn Shanghai 201109 Peoples R China

The problem of joint observation and transmission scheduling with energy constraint is very important, but has received limited attention so far. To address the joint scheduling problem considering energy consumption and supplement, a new constraint satisfaction model with energy constraint for multiple agile satellites is established. Then a Local Search Enhanced ant colony optimization with Multi-Knapsack Task Assignment (LSE-ACO-MKTA) algorithm is proposed, integrating observation, transmission, and charging into a unified planning framework. The algorithm employs a multi-knapsack-based task assignment strategy and local search enhanced ACO algorithm, significantly reducing the dimension of the original problem. The first simulation experiment validated the necessity of joint scheduling with energy constraint. After that, two comparative experiments were conducted to discuss the mechanism of local search and task assignment and then proved the efficiency of LSE-ACO-MKTA.

关键词： Agile satellites Joint scheduling Energy constraint Constraint satisfaction model ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A low-carbon scheduling method based on improved ant colony algorithm for underground electric transportation vehicles

引用

COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-18页

作者： Zhang, Yizhe Guo, Yinan Huang, Yao Ge, Shirong China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China Minist Emergency Management Key Lab Intelligent Min & Robot Beijing 100083 Peoples R China

Improved scheduling of underground transportation vehicles in coal mines can significantly enhance work efficiency and contribute to safer production. However, the specific working conditions and limitations of electric vehicles pose significant challenges to effective vehicle scheduling. To address this issue, a constrained single-objective optimization model is developed to minimize transportation costs for low-carbon scheduling of underground electric transportation vehicles (ETVs). The model incorporates constraints related to load capacity, cruising range, and safety regulations. A specific energy consumption model for ETVs is formulated, considering factors such as road conditions, load, and driving state. To solve this problem, an improved ant colony optimization algorithm integrated with Q-learning (ACO-QL) is proposed. Specifically, ant colony optimization explores the global solution space and identifies promising regions, while the split strategy effectively distributes demand across multiple vehicles. Q-learning enhances local search by selecting the most appropriate operator, preventing premature convergence to local optima. Experimental results on four real-world instances demonstrate the superior performance of ACO-QL compared to state-of-the-art algorithms.

关键词： Q-learning ant colony optimization algorithm Coal mine Underground electric transport vehicle Low-carbon scheduling

来源：评论

学校读者我要写书评

暂无评论

Multiobjective Trade-Off optimization of Time, Cost, Quality, and Carbon Emission in the Building Construction Stage

引用

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2025年第4期151卷

作者： Wang, Haixin Chen, Xin Wang, Jiaqi Guan, Wenfeng Wei, Shengsong Shandong Univ Sci & Technol Coll Civil Engn & Architecture Qingdao 266590 Peoples R China Qingdao Huanghai Univ Coll Architecture & Engn Qingdao 266555 Peoples R China Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Hong Kong 999077 Peoples R China

For a long time, the production management of the construction industry has been mostly based on experience, and there are often problems such as construction delays, high costs, quality defects, and serious pollution. In order to solve these problems, this paper introduces the quantitative methods of time, cost, quality, and carbon emission in the construction stage, constructs an optimization model of duration, cost, quality, and carbon emissions, and uses the ant colony optimization algorithm to solve the problem. Finally, through model simulation, the Pareto optimal solution is obtained, which proves the effectiveness of the method proposed in this paper. The research results of this paper can provide a feasible solution for decision-makers at construction sites, guiding on-site construction to achieve better production efficiency. At the same time, it offers a reference for construction management under the premise of low carbon in the construction industry. In addition, the method for quantifying carbon emissions during the construction phase proposed in this study can also provide a theoretical reference for future research on carbon emissions in the construction sector.

关键词： Engineering project management Multiobjective trade-off optimization ant colony optimization algorithm Construction management Carbon emissions

来源：评论

学校读者我要写书评

暂无评论

optimization of Flight Endurance for Turboprop Air Taxis Using Metaheuristic algorithms

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1685-1698页

作者： Talouki, I. Fozouni Toloei, A. Shahid Beheshti Univ Dept New Technol & Aerosp Engn Tehran Iran

This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: Grey Wolf optimization (GWO) and ant colony optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft Piper, Beechcraft, and Bombardier was examined compared to their original endurance. In this context, the ant colony optimization algorithm exhibited better performance than the Grey Wolf optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.

关键词： Air Taxi optimization Gradient Descent Grey Wolf optimization algorithm ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Energy-Balanced Routing algorithm Based on ant colony optimization for Mobile Ad Hoc Networks

引用

SENSORS 2018年第11期18卷 3657-3657页

作者： Yang, Dong Xia, Hongxing Xu, Erfei Jing, Dongliang Zhang, Hailin Xidian Univ State Key Lab Integrated Serv Network Xian 710071 Peoples R China Nantong Normal Coll Nantong 226010 Peoples R China

The mobile ad hoc network (MANET) is a multi-hop, non-central network composed of mobile terminals with self-organizing features. Aiming at the problem of extra energy consumption caused by node motion in MANETs, this paper proposes an improved energy and mobility ant colony optimization (IEMACO) routing algorithm. Firstly, the algorithm accelerates the convergence speed of the routing algorithm and reduces the number of route discovery packets by introducing an offset coefficient of the transition probability. Then, based on the energy consumption rate, the remaining lifetime of nodes (RLTn) is considered. The position and velocity information predicts the remaining lifetime of the link (RLTl). The algorithm combines RLTn and RLTl to design the pheromone generation method, which selects the better quality path according to the transition probability to ensure continuous data transmission. As a result, the energy consumption in the network is balanced. The simulation results show that compared to the Ad Hoc on-demand multipath distance vector (AOMDV) algorithm with multipath routing and the ant Hoc Max-Min-Path (antHocMMP) algorithm in consideration of node energy consumption and mobility, the IEMACO algorithm can reduce the frequency of route discovery and has lower end-to-end delay as well as packet loss rate especially when nodes move, and can extend the network lifetime.

关键词： energy constraint ant colony optimization algorithm convergence remaining lifetime

来源：评论

学校读者我要写书评

暂无评论

Energy-Aware Clustering in the Internet of Things using Tabu Search and ant colony optimization algorithms

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第12期14卷 370-376页

作者： Li, Mei Ai, Jing Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China Wuhan Design Engn Coll Informat Engn Coll Wuhan 430000 Hubei Peoples R China

Internet of Things (IoT) significantly impacts communication systems' efficiency and the requirements for applications in our daily lives. Among the major challenges involved in data transmission over IoT networks is the development of an energy-efficient clustering mechanism. Recent methods are challenged by long transmission delays, imbalanced load distribution, and limited network lifespan. This paper suggests a new cluster-based routing method combining Tabu The TS algorithm overcomes the disadvantage of ACO, in which ants move randomly throughout the colony in search of food sources. In the process of solving optimization problems, the ACO algorithm traps ants, resulting in a considerable increase in the time required for local searches. TS can be used to overcome these drawbacks. In fact, the TS algorithm eliminates the problem of getting stuck in local optima due to the randomness of the search process. Experimental results indicate that the proposed hybrid algorithm outperforms ACO, LEACH, and genetic algorithms regarding energy consumption and network lifetime.

关键词： Internet of things clustering data transmission energy efficiency ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An ant colony optimization-Simulated Annealing algorithm for Solving a Multiload AGVs Workshop Scheduling Problem with Limited Buffer Capacity

引用

PROCESSES 2023年第3期11卷 861页

作者： Wang, Zishi Wu, Yaohua Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

In this paper, we address a multiload AGVs workshop scheduling problem with limited buffer capacity. This has important theoretical research value and significance in the manufacturing field in considering the efficient multiload AGVs widely used today, and in the limited buffer area in production practice. To minimize the maximum completion time, an improved ant colony optimization-simulated annealing algorithm based on a multiattribute dispatching rule is proposed. First, we introduce a multiattribute dispatching rule, which combines two attributes, delivery completion time and input queue through dynamic weights that are determined by the information about the system, using the multiattribute dispatching rule to construct the initial solution. Then, with the ant colony optimization-simulated annealing algorithm as the basic framework, we propose a method for calculating transfer probability based on the multiattribute dispatching rule, which obtains heuristic information through the proposed rule. Further, we propose a path branch mechanism and dynamic equilibrium mechanism, aiming to efficiently construct the ant path and dynamically adjust ant path distribution. We propose a key job strategy and design a 2-opt neighborhood search method based on key jobs. Data experiments demonstrate the multiattribute dispatching rule is superior over other heuristic dispatching rules;the algorithm improvement strategies proposed are effective when used simultaneously or separately. Further, the proposed algorithm in this paper is superior over other heuristic algorithms and adapts to all kinds of instances.

关键词： multiload AGV limited buffer capacity multiattribute dispatching rule ant colony optimization algorithm simulated annealing algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：