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6th International Conference on Natural Language Processing (ICNLP)

作者： Yang, Yanzhu Yang, Wujun Chang, Zhixian Xian Univ Posts & Telecommun Dept Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Time-Sensitive Network (TSN) is a type of deterministic Ethernet that can satisfy the need for deterministic transmission of data in many industries. TSN provides bounded delay for Time Triggered (TT) communication by providing global scheduling of transmission messages. Therefore, to address the problem of delay optimization for path selection of TT flows in industrial TSN, this paper puts forward a kind of oriented industrial TT flows routing multipath time delay difference of perception of transport mechanism. A multipath forwarding metric is designed based on the sending delay and processing delay, and then the multipath forwarding problem is transformed into a 0-1 Integer Linear Programming (ILP) problem. Finally, based on the ant colony optimization (ACO) algorithm, the refined algorithm (RACO) offers reliable deterministic end-to-end delay guarantees for TT flows in the industrial Internet. Through a large number of simulation experiments and analysis of the experimental results, this scheme realizes the low-latency parallel transmission of flows and achieves lower end-to-end delay, with faster convergence speed and stronger global optimization capability, which can more effectively avoid the possibility of falling into a stagnant state.

关键词： time-sensitive network time-triggered flow path selection strategy ant colony optimization algorithm

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Model Specification Searches in Structural Equation Modeling with a Hybrid ant colony optimization algorithm

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STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 2022年第5期29卷 655-666页

作者： Jing, Zeyuan Kuang, Huan Leite, Walter L. Marcoulides, Katerina M. Fisk, Charles L. Univ Florida Gainesville FL 32653 USA Univ Minnesota Minneapolis MN 55455 USA Univ Maryland College Pk MD 20742 USA

Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid ant colony optimization algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the ant colony optimization algorithm with the Tabu search algorithm to increase both accuracy and efficiency. A Monte Carlo simulation study showed that the hACO algorithm provided accurate and efficient SEM specification searches across all designed simulation conditions. The hACO algorithm can help applied researchers conduct specification searches while avoiding potential model misspecifications.

关键词： ant colony optimization algorithm data mining metaheuristic algorithm model misspecification specification search structural equation modeling Tabu Search

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Practice System of ant colony optimization algorithm in Business Administration 3rd

Practice System of Ant Colony Optimization Algorithm in Busi...

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International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP)

作者： Ma, Deyong Wang, Jun Hainan Univ Haikou Hainan Peoples R China

ISBN: (纸本)9789811978449;9789811978425;9789811978418

AS a result of routine businessmanagement in the practical application of such problems as low efficiency, the content is not fine, so if you want to optimize the effect of market regulation, improve business management efficiency, and ensure quality of market operation, need to be in the original content on the basis of reasonable use of information technology management idea, this is also discusses the main problems of the current market industry. Based on the application of particle swarm optimization (PSO), this paper integrates it with ant colony algorithm, and then uses the whole process of fine management mode to carry out visual and cyclic supervision and scheduling of the whole market inspection process. The results of this study can improve actual work efficiency, strengthen industrial and commercial scheduling management, and reduce the work pressure faced by industrial and commercial personnel. It is thework content of enterprise management that is more standardized.

关键词： ant colony optimization algorithm Particle Swarm optimization Business Administration Market Regulation

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ant colony optimization algorithm in the Design of University Subject Teaching Platform 22

Ant Colony Optimization Algorithm in the Design of Universit...

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Proceedings of the 7th International Conference on Information Systems Engineering

作者： Guomin Fang Qujing Medical College China

ISBN: (纸本)9781450397889

The development of educational informatization has greatly changed the current educational model of colleges and universities (CAU). The use of informatization technology combined with the characteristics of disciplines to build a TP to promote the development of informatization teaching. This paper adopts the B/S model to design the framework of the university subject TP. The user only needs to install a browser to carry out teaching and learning activities, which simplifies the use of the system. With the support of Web technology, the teaching platform (TP) can not only realize teaching evaluation, but also manage and store subject resources. In the process of platform login, this paper uses the characteristics of ant colony optimization (ACO) algorithm to match users with password strings with high security factors, ensuring the system Login security.

关键词： Teaching Evaluation Subject Teaching Platform ant colony optimization algorithm B/S Model

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A Transmission Line Planning Method Based on Variable Size Grid and Improved ant colony optimization algorithm 38

A Transmission Line Planning Method Based on Variable Size G...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Liu, Zhuo Nan, Guojun Cheng, Yingying Du, Haibo Zhu, Wenwu Hefei University of Technology School of Electrical Engineering and Automation Hefei China

ISBN: (纸本)9798350303636

Route planning for transmission lines has always been an important part of the power construction industry. In the past, however, such planning has involved many critical steps, not only collecting complex geographic information data, but also performing personnel calculations. The collected data needed to be analyzed by engineers, and then safety and reliability were taken into account to determine the lowest economic cost route option. This traditional approach is not only very consuming for personnel, but also lacks reliability. This is because the development of the scheme is too dependent on the experience and decision making of the personnel. Now this paper proposes an intelligent route selection algorithm that improves on the ant colony optimization (ACO) algorithm. Unlike previous route planning algorithms that are based on fixed size grid (FSG) maps, this paper uses variable size grid (VSG) maps to replace the maps with fixed-size grids. This not only reduces the number of grids in the route search to speed up the search speed, but also better fits the terrain to improve the rationality of the route. Then the grid node structure of the variable size grid is optimized. Then the heuristic function of the ACO algorithm is improved, and the parallel search based on variable-size grids is adopted. This not only improves the efficiency, but also increases the chance of searching the global optimal route. Finally, route optimization is performed on the routes to achieve smoothing process and reduce the cost of the towers. © 2023 IEEE.

关键词： ant colony optimization algorithm parallel search smoothing process variable size grid

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Security Test Case Prioritization through ant colony optimization algorithm

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Computer Systems Science & Engineering 2023年第12期47卷 3165-3195页

作者： Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Mohd Waris Khan Mohd Faizan Alka Agrawal Dhirendra Pandey Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Computer Application Integral UniversityLucknowUttar Pradesh226026India Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknowUttar Pradesh226025India

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software *** of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test *** effectively applying test case prioritization,both the time and cost required for developing secure software can be *** paper proposes a test case prioritization technique based on the ant colony optimization(ACO)algorithm,a metaheuristic *** performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional *** has been applied to a Mobile Payment Wallet application to validate the proposed *** results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD *** ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering *** findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

关键词： Confidentiality integrity authentication non-repudiation resilience authorization ant colony optimization algorithm

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Design of Rural Ambient Intelligence Monitoring System Based on ant colony optimization algorithm 1

Design of Rural Ambient Intelligence Monitoring System Based...

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

作者： Li, Shuai Ye, Jingyu Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350316469

The rural information monitoring system based on wireless sensor network (WSN) technology can be precisely used for monitoring the rural environment. In a data-centric WSN, to ensure accurate information transmission, channel equalization technology is needed to solve this problem. Optimizing routing protocols can balance network energy consumption and prevent important nodes from being consumed too early. Reasonable node deployment can not only reduce energy consumption but also promote network communication. Therefore, in order to improve the communication quality of WSN, this paper used ant colony optimization (ACO) algorithm to improve the communication quality of WSN, and designed a rural ambient intelligence monitoring system according to this algorithm. Finally, data reliability and communication quality tests were conducted on the intelligent monitoring system. The experimental results showed that the error between the air humidity collected by the rural ambient intelligence monitoring platform and the manually sampled air humidity was not more than 0.5%, the error of measuring the PM2.5 content in the air was not more than 0.5mcg, and the network packet loss rate was always below 5%. The results of many tests showed that the system runs stably and has high monitoring accuracy, which can meet the needs of rural ambient intelligence monitoring. © 2023 IEEE.

关键词： ant colony optimization algorithm Intelligent Detection Rural Environment Wireless Sensor Network

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Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning

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KNOWLEDGE-BASED SYSTEMS 2024年 288卷

作者： Cui, Junguo Wu, Lei Huang, Xiaodong Xu, Dengpan Liu, Chao Xiao, Wensheng China Univ Petr East China Coll Mech & Elect Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Marine Geophys Prospecting & Exp Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore

As a widely used path planning algorithm, the ant colony optimization algorithm (ACO) has evolved into a welldeveloped method within the realm of optimization algorithms and has been extensively applied across various fields. In this study, a multi-strategy adaptable ant colony optimization (MsAACO) is proposed to alleviate the insufficient and inefficient convergence of ACO, employing four-design improvements. First, a directionguidance mechanism is proposed to improve the performance of node selection. Second, an adaptive heuristic function is introduced to decrease the length and number of turns of the optimal path solutions. Moreover, the deterministic state transition probability rule was employed to promote the convergence speed of ACO. Finally, nonuniform pheromone initialization was utilized to enhance the ability of ACO to select advantageous regions. Subsequently, the major parameters of the strategies were optimized and their effectiveness was validated. MsAACO was proposed by combining these four strategies with ACO. To verify the advantages of MsAACO, five representative environment models were employed, and comprehensive experiments were conducted by comparing them with existing approaches, including the A* algorithm, variants of ACO, Dijkstra's algorithm, jump point search algorithm, best-first search, breadth-first search, trace algorithm, and other excellent algorithms. The experimental statistical results demonstrate that MsAACO can efficiently generate smoother optimal path-planning solutions with lower length and turn times and improve the convergence efficiency and stability of ACO compared to other algorithms. The generated results of MsAACO verified its superiority in solving the pathplanning problem of mobile robots.

关键词： Path planning ant colony optimization algorithm Directional mechanism Adaptive updating

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Knowledge-driven ant colony optimization algorithm for vehicle routing problem in instant delivery peak period

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Hou, Ying Guo, Xinyu Han, Honggui Wang, Jingjing Beijing Univ Technol Fac Informat Technol Engn Res Ctr Digital Community Beijing Lab Urban Mass TransitMinist Educ Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China

Instant delivery is an important part of urban logistics distribution, which realizes point-to-point distribution between merchants and customers. During the peak period of orders, instant delivery is a large-scale variable NP-hard combinatorial optimization problem, which increases the difficulty and complexity of scheduling greatly. To solve the large-scale vehicle routing problem of instant delivery in peak periods, a knowledge-driven ant colony optimization (KDACO) algorithm is proposed in this paper. First, the knowledge base is established to guide evolutionary search, including the knowledge of order priority and the feature knowledge of feasible schemes. Second, the pheromone supplementation strategy is designed based on the knowledge of order priority, enhancing the guiding ability of the pheromone table. Third, the adaptive evolutionary operator is designed based on the feature knowledge of feasible schemes, improving the optimization efficiency of the algorithm. Finally, numerical experiments on extensive classical datasets show that the proposed KDACO can obtain superior performance to other state-of-the-art algorithms in the instant delivery peak period. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： ant colony optimization algorithm Vehicle routing problem Instant delivery Peak period

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An improved heuristic mechanism ant colony optimization algorithm for solving path planning

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KNOWLEDGE-BASED SYSTEMS 2023年第1期271卷

作者： Liu, Chao Wu, Lei Xiao, Wensheng Li, Guangxin Xu, Dengpan Guo, Jingjing Li, Wentao China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Offshore Geophys & Explorat Equi Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore Macao Polytech Univ Fac Appl Sci Taipa 999078 Macao Peoples R China CNOOC Safety & Technol Serv Co Ltd Tianjin 300450 Peoples R China

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.

关键词： Path planning Artificial intelligence algorithm ant colony optimization algorithm Heuristic mechanism Pheromone evaporation

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