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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Peng, Wenliang Wang, Lili Yang, Huizhan Lu, Guiping Beijing Institute of Technology Zhuhai Zhuhai China

ISBN: (纸本)9798350380989

ant colony algorithm was proposed by Marco Dorigo in his doctoral thesis in 1992, inspired by the behavior of ant colonies discovering paths while searching for food. In the process of studying ant foraging, ants release a substance called 'pheromone' along their path. ants in the ant colony have the ability to perceive 'pheromone' and walk along paths with higher concentrations of 'pheromone'. Each passing ant leaves behind 'pheromone' on the road, forming a mechanism similar to positive feedback. The entire ant colony will follow the shortest path to reach the food source. © 2024 IEEE.

关键词： ant colony algorithm intelligent search meta heuristic pheromone positive feedback

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Hybrid ant colony algorithm Using Improved Circle Strategy for TSP Problem

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-16页

作者： Li, Qingshun Dong, Xueshi Guo, Qingteng Qingdao Univ Qingdao Shandong Peoples R China

Based on the problems existing in the traditional ant colony algorithm in solving the traveling salesman problem, a hybrid ant colony algorithm combining the improved circle strategy and the ant colony algorithm is proposed. In the proposed hybrid ant colony algorithm, an improved circle strategy is used to optimize the solution obtained by the ant colony algorithm, so as to improve the search efficiency and search ability. At the same time, the uniform design method is used to find the optimal parameter combination of the algorithm. The improved circle strategy is based on the nearest neighbor strategy to optimize the solution obtained by the ant colony algorithm into a better solution. This paper uses eight standard instances in the TSPLIB standard library to experimentally verify the algorithm. The experimental results show that the proposed hybrid ant colony algorithm can effectively improve the convergence ability of the algorithm, obtain higher quality solutions, and have better optimization ability and stability for solving TSP problems.

关键词： ant colony algorithm Heuristic algorithm Improved Circle algorithm TSP Uniform Design

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Mobile Robot Path Planning Based on Angle-Guided ant colony algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-19页

作者： Li, Yongsheng Huang, Yinjuan Ge, Lina Li, Xi Guangxi Univ Nationalities Nanning Peoples R China

ant colony algorithm is easy to fall into local optimum, and its convergent speed is slow in solving mobile robot path planning. Therefore, an ant colony algorithm based on angle guide is proposed in this paper to solve the problems. In the choice of nodes, the authors integrate the angle factor into the heuristic information of the ant colony algorithm to guide the ants' search direction and improve the search efficiency. The pheromone differential updating is carried out for different quality paths, and the pheromone chaotic disturbance updating mechanism is introduced. Then the algorithm can make full use of the better path information and maintain a better global search ability. According to simulations, its global search is strong, and it can range out of local optimum, and it is a fast convergence to the global optimum. The improved algorithm is feasible and effective.

关键词： Angle Guided ant colony algorithm Chaos Genetic algorithm Grid Method Mobile Robot Particle Swarm Optimization Path Planning Pheromone

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Recognition of Self-organized Aggregation Behavior in Social Networks Based on ant colony algorithm 1

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6th European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Hu, Nan Li, Hongjian China Univ Labor Relat Beijing 100048 Peoples R China

ISBN: (数字)9783031288678

ISBN: (纸本)9783031288661;9783031288678

In order to effectively detect the real network community structure and improve the accuracy of user stage partitioning to the corresponding self-organized community, a self-organized clustering behavior recognition method based on ant colony algorithm is proposed. According to user's individual attribute and collaborative attribute, the node with high aggregation coefficient under user's knowledge quality scale is chosen as the core to construct social network aggregation behavior community. The evolutionary types of group trajectory are divided into seven types. ant colony algorithm is used to track the group trajectory. Abstract tagged basic events from user attributes, establish recognition model to identify abnormal behavior, and realize self-organized aggregation behavior recognition in social network. Experimental results show that the self-organized aggregation recognition method based on ant colony algorithm can get more reasonable group structure, better quality of community partition, and improve the accuracy of user stage partition to the corresponding self-organized community.

关键词： ant colony algorithm Social network Self-organization Aggregation behavior Behavior recognition User characteristics

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Path Planning of an Inchworm-like Robot in 3D Environment Based on Improved ant colony algorithm 2

Path Planning of an Inchworm-like Robot in 3D Environment Ba...

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2nd IEEE-Industrial-Electronics-Society Annual On-Line Conference (ONCON)

作者： Sun, Zhaohua Tan, Jianping Wang, Pengfei Li, Kuangxi China Univ Petr Sch Engn Beijing Karamay Peoples R China

ISBN: (纸本)9798350357974

To solve the global path planning of a 7-degree-of-freedom inchworm robot in a 3D hybrid environment of "pipe + wall", a planning method based on the A*-inspired ant colony algorithm is proposed. First of all, the coordinates of the reachable points are obtained by modelling the environment, and the set of reachable points is determined by combining with the workspace of the inchworm robot. Then, a heuristic function based on the A* algorithm is designed to improve the convergence speed of the algorithm;at the same time, a bending suppression operator is added to the heuristic function to reduce the problems of a large number of bending times and large cumulative bending angle caused by the A* algorithm. Finally, the maximum minimum ant system (MMAS) is introduced to solve the premature phenomenon that the traditional ACO algorithm may cause. The simulation results show that the method suits the three-dimensional environments of pure pipe, pure wall and "pipe+wall".

关键词： inchworm robot walkable point of assembly ant colony algorithm three-dimensional environments

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Multiple ant colony algorithm Combining Community Relationship Network

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第8期47卷 10531-10546页

作者： Zhao, Jiabo You, Xiaoming Duan, Qianqian 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

ant colony algorithm can better deal with combinatorial optimization problems, but it is still difficult to balance the solution accuracy and convergence speed facing large-scale TSP. Nowadays, most scholars focus on the route information of better ants for improvement, while ignoring the route information of general ants with a large base. So, this study proposes the multiple ant colony algorithm combining community relationship network (CACO) by collecting route information of all ants and constructing a route relationship network to improve the accuracy of the solution. The network is divided into a number of small communities that reflect the affinity of multiple colony ants to different cities through community detection with modularity. Within the communities, CACO use the excellent roue exploration ability of the ant colony algorithm to identify high-quality route segments, integrating the pheromones of high-quality segments in the communities to provide pheromone feedback to the multiple colony ants for better route exploration. The three parts of route information collection, community detection and pheromone feedback form a feedback loop, which keeps cycling when multiple populations ants explore, and each cycle will drive the result closer to the optimal solution. Meanwhile, CACO proposes a mutual assistance strategy to improve the exploration ability of multiple colony ants by complementing each other according to the different states of superior and inferior populations. To test the performance of CACO, 28 TSP instances are compared with the well-known improved algorithms are compared and results show CACO outperforms other improved algorithms significantly, especially in large-scale TSP.

关键词： ant colony algorithm Modularity Community detection Route relation network TSP

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Investigating the mitigation of greenhouse gas emissions from municipal solid waste management using ant colony algorithm, Monte Carlo simulation and LCA approach in terms of EU Green Deal

Waste Management Bulletin

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Waste Management Bulletin 2023年第2期1卷 6-14页

作者： Pamukçu, Hale Soyertaş Yapıcıoğlu, Pelin İrfan Yeşilnacar, Mehmet Harran University Graduate School of Natural and Applied Sciences Environmental Engineering Department Sanliurfa Turkey Harran University Engineering Faculty Environmental Engineering Department Sanliurfa Turkey

This study majorly aimed to determine the effect of optimization on transport routes on the reduction of greenhouse gas (GHG) emissions from municipal solid waste management (MSM) within the scope of European Union (EU) Green Deal. Optimization of collection and transportation routes has been regarded as an effective methodology in order to mitigate the GHG emissions of municipal waste management, recently. Optimization of routes has been obtained using ant colony algorithm (ACA) and Monte Carlo simulation, in this study. In this context, this study investigated to reduce GHG emissions from municipal waste management using optimization of transportation routes in Diyarbakir city in Turkey. Firstly, GHG emissions which are carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) emissions from waste collection and transport have been calculated using a new developed model based on Tier-I method. Then, Monte Carlo simulation has been used to figure out the GHG emissions. Finally, life cycle assessment (LCA) approach has been applied to determine the GHG emissions. According to the route optimization resulting ACA methodology, nearly 47.43% of reduction on each GHG emissions. Approximately, 58%, 38% and 51% of reduction on CO₂, CH₄ and N₂O emissions respectively has been achieved, in the result of the route optimization using Monte Carlo simulation. The results of LCA methodology revealed that the reduction reached up 45.71% on GHG emissions in terms of Global Warming Potential (GWP). The reduction amounts have been overlapped with each other. © 2023 The Authors

关键词： ant colony algorithm European Green Deal Greenhouse Gas Monte Carlo Simulation Municipal Solid Waste Management Reduction

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Tourism route optimization based on improved knowledge ant colony algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2022年第5期8卷 3973-3988页

作者： Li, Sidi Luo, Tianyu Wang, Ling Xing, Lining Ren, Teng Cent South Univ Forestry & Technol Sch Foreign Languages Changsha 410004 Peoples R China Cent South Univ Forestry & Technol Coll Logist & Transportat Changsha 410004 Peoples R China

With the rapid development of tourism in the economy, popular demand for tourism also increases. Unreasonable distribution arises a series of problems such as reduction of tourist satisfaction and decrease of the income in tourist attractions. Based on consideration of tourism route planning, a mathematical model which takes the maximization of the overall satisfaction of all tourist groups as the objective function is established by taking the age and preferences of tourists, the upper limits of the tourist carrying capacity in various tourism routes, etc. as constraints. It aims to maximize income in tourist attractions while improving tourist satisfaction. Based on the tourist data of a travel agency, the statistical ideas of hierarchical clustering and random sampling are utilized to process the acquired data to obtain the simulation examples in the article. Aiming at this model, a knowledge-based hybrid ant colony algorithm is designed. On this basis, the mechanism of bacterial foraging algorithm is introduced. It improves the performance of the algorithm and avoids the generation of local optimal solution. At the same time, two knowledge models are in addition to improve the solution quality of the algorithm. Typical simulation indicates that the improved ant colony algorithm can find the optimal solution at a higher efficiency when solving the tourism route planning problem. The model can also satisfy the economic benefit of enterprises and achieves favorable path optimization effect under different optional routes, thus further verifying the effect liveness of the model.

关键词： Tourism route planning Tourist satisfaction ant colony algorithm Bacterial foraging algorithm Knowledge models

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Multi-Robot Path Planning based on ant colony algorithm

Multi-Robot Path Planning based on Ant Colony Algorithm

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

作者： Li, Li Jin, Kan Zou, Xinye Wuhan Technology and Business University Wuhan430065 China

ISBN: (纸本)9798350308167

With the continuous development of science and technology, people's requirements for robots are no longer limited to the research of single robots. With the emergence of robot production lines and the application of flexible manufacturing systems, the research of multi-robot has become an important direction of robotics research. Therefore, the role of robot path planning in the coordination and cooperation of multi-robot groups is very important, but there is a problem of inaccurate evaluation of results. The traditional algorithm cannot solve the path planning problem in the coordination and cooperation of multi-robot groups, and the evaluation is unreasonable. Therefore, this paper proposes an ant colony algorithm for innovative path planning analysis. First, the intelligent theory is used to evaluate the robot, and the indicators are divided according to the path planning requirements to reduce them Interference factors in path planning. Then, a multi-robot path planning scheme is formed, and the path planning results are comprehensively analyzed. MATLAB simulation shows that under the condition of certain evaluation criteria, the ant colony algorithm has the accuracy and path planning of multi-robot path planning the time is better than the traditional algorithm. © 2023 IEEE.

关键词： ant colony algorithm Collaboration path planning robot

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Application Analysis of ant colony algorithm in Engineering Management 3

Application Analysis of Ant Colony Algorithm in Engineering ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ding, Lijuan Hubei Three Gorges Polytechnic Hubei Yichang443000 China

ISBN: (纸本)9798350319125

With the increasing complexity and scale of engineering projects, how to plan and organize engineering projects effectively becomes more and more important. Traditional engineering management methods often need to consume a lot of time and manpower, and it is difficult to solve complex optimization problems. In recent years, the rapid development of artificial intelligence technology has brought new opportunities and challenges to project management. Among them, ant colony algorithm, as a new intelligent optimization algorithm, has a wide application prospect and important significance in engineering management. © 2023 IEEE.

关键词： ant colony algorithm artificial intelligence engineering construction Engineering management

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