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improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective

EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1313-1326页

作者： Pu, Xingcheng Song, Xinlin Tan, Ling Zhang, Yi Tongling Univ Coll Math & Comp Sci Tongling 244061 Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

In order to solve the problem of path planning of a single robot and multi-Robots with multi-objective, an improved ant colony algorithm is *** the improved ant colony algorithm, both local and global optimal searching are considered. In local optimal searching, gradient distribution is used to initialize the pheromone concentration to improve the convergence speed of the algorithm. The self-adaptive impact factor is used to enhance better fitness of the algorithm in different environments. In global optimal searching, a reward and punishment mechanism has been established to improve the pheromone update strategy. Furthermore, a node rearrangement strategy is adopted to increase the diversity of solutions. According to the improved colony algorithm, the problem of path planning of a single robot and multi-Robots with multi-objective can be solved. In two different scenarios, compared experiments show that the improved colony algorithm can obtain better path length, convergence speed and running time than traditional ant colony algorithm (ACO) and the improved ant colony algorithm (IACO-SFLA) in path planning of a single robot. Simulation results also show that the improved ant colony algorithm has good ability in multi-objective path planning. Especially, all robots can safely reach the targets by moving simultaneously and obstacles can be effectively avoided too.

关键词： improved ant colony algorithm Multi-robots Path planning Obstacle avoidance

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improved ant colony algorithm for the mixed-model parallel two-sided assembly lines balancing problem

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ENGINEERING OPTIMIZATION 2024年第11期56卷 1784-1798页

作者： Jiao, Yuling Wang, Yang Deng, Xue Su, Xinyue Huang, Lujiao Jilin Univ Coll Transportat Changchun Peoples R China

In response to the increasing demand for individualized products in the 'small-lot, multi-variety' market, there is an urgent need to enhance the efficiency and intelligence level of assembly lines. To address this, a novel solution method for the mixed-model parallel two-sided assembly lines balancing problem (MMPTSALBP) is proposed. The first step is to define the type and layout of the parallel two-sided assembly lines. Next, a mathematical model is constructed with the objective of minimizing the number of workstations required for the MMPTSALBP, proposing an improved ant colony algorithm that utilizes double string representation for the initial solution encoding method and a double pheromone matrix update to solve the model. Its efficiency is tested on benchmark datasets according to some performance measures and classifications. A study validates its effectiveness on 25 classical arithmetic cases by comparison with the solutions of an heuristic algorithm and an artificial fish swarm algorithm that had been reported to perform well.

关键词： Logistics engineering parallel two-sided assembly lines mixed-model assembly line balancing improved ant colony algorithm

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improved ant colony algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

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Journal of Harbin Institute of Technology(New Series) 2023年第1期30卷 1-12页

作者： Yaping Zhang Ye Chen Yu Zhang Jian Mao Qian Luo School of Transportation Science and Engineering Harbin Institute of TechnologyHarbin 150001China Beijing E-hualu Information Technology Co.Ltd. Beijing 100043China Chengdu Civil Aviation Information Technology Co.Ltd. Chengdu 610041China

Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle *** model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and *** results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle ***,the improved ACO can jump out of local optima,which can balance the working time of refueling *** research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.

关键词： airport surface traffic ground service support vehicle scheduling topology model improved ant colony algorithm response value

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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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Communication optimisation of smart agriculture wireless sensor network based on improved ant colony algorithm

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2024年第3-4期15卷 211-219页

作者： Lin, Zhihui Xian Siyuan Univ Coll Elect Informat Engn Xian 710038 Shaanxi Peoples R China

Wireless Sensor Network (WSN) is a distributed sensor network that has emerged in recent years. It has a wide range of applications and is also very important for the optimisation and transformation of traditional agriculture. Based on the research on ant colony optimisation algorithm, this article analyses wireless sensor network routing technology, and studies the impact of improving ant colony algorithm on agriculture from the perspective of improving network security and reducing network energy consumption. Research has found that the current agricultural output value is only about 250,000 yuan, which is difficult to meet farmers' income expectations. However, by using improved ant colony algorithm and wireless sensor technology to realise intelligent transformation of farms, agricultural output and profits can be improved.

关键词： improved ant colony algorithm smart agriculture wireless sensors network communication optimisation

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Application of data mining based on improved ant colony algorithm in college students' employment and entrepreneurship education

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SOFT COMPUTING 2023年 1-10页

作者： Zhang, Yingnan Jilin Agr Univ Student Affairs Dept Changchun 130000 Jilin Peoples R China

Under the background of the new era of economy, the employment and entrepreneurship education of college students has also ushered in new problems. In the construction of student employment and entrepreneurship data mining model, data solving efficiency and association rule mining play a key role in the performance of the model. How to optimize and improve the mathematical model and improve the accuracy of student employment and entrepreneurship data analysis needs further research and analysis. This paper constructs the mathematical model of ant colony algorithm, introduces the logical process in solving practical problems, provides setting suggestions for the value of algorithm parameters, and improves the solution efficiency. This paper improves the decision tree classification method and ant colony algorithm. The association rule mining based on ant colony has more perfect reference value. From the combination and interest analysis of college students' employment and entrepreneurship content, it provides efficient, practical and useful information for the school. Finally, after constructing the prediction model, this paper analyzes the four factors affecting the choice of college students' homework entrepreneurial intention, which are individual, family, school and society, and puts forward five measures to improve college students' entrepreneurship and employment education.

关键词： improved ant colony algorithm Data mining College student Employment and entrepreneurship education

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A Balanced Scheduling Method of Smart City Enterprise Resource Information Based on improved ant colony algorithm

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JOURNAL OF TESTING AND EVALUATION 2023年第3期51卷 1265-1276页

作者： Chen, Suqing Jining Normal Univ Comp & Big Data Dept 59 Gongnong St Ulanqab 012000 Peoples R China

In order to address the problems of low resource utilization rate and poor scheduling balance in current enterprise resource information balanced scheduling, an enterprise resource information balanced scheduling method based on an improved ant colony algorithm (ACO) was proposed. The ACO framework is introduced in this algorithm to establish a balanced scheduling model of enterprise resource information. By adding a mapping algorithm of a virtual machine and physical machine, an improved algorithm of load balancing between nodes is proposed based on the ACO. Forward ants detect node types, record node information, and leave foraging pheromones when they encounter load nodes. The backward ants trace back to the load node according to the tracking pheromone and allocate the overloaded node task reasonably. In the search process, the path pheromone is dynamically modified according to the node type, and the analysis of enterprise resource information balance scheduling algorithm is completed. The experimental results show that this method has good balance of resource information scheduling and can effectively improve resource utilization.

关键词： improved ant colony algorithm enterprise resources information balance balanced scheduling model

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An improved ant colony algorithm for Virtual Resource Scheduling in Cloud Computing Methods to Improve the Performance of Virtual Resource Scheduling

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 249-261页

作者： Zhong, Chunlei Yang, Gang Huaian Bioengn Branch Inst Jiangsu Union Tech Inst Huaian 223200 Peoples R China Wenzhou Univ Coll Teacher Educ Wenzhou 325035 Peoples R China

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR -Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

关键词： improved ant colony algorithm cloud computing virtual resources intelligent scheduling

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Optimization of Milling Paths for Engine Remanufacturing Cylinder Block External Attachment Mounting Surface Based on improved ant colony algorithm 5

Optimization of Milling Paths for Engine Remanufacturing Cyl...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Zheng, Yu Lug, Zhongdong Shanghai Dianji Univ Coll Mech Shanghai Peoples R China

ISBN: (纸本)9798350363272;9798350363265

This paper presents an enhanced ant colony algorithm to optimize the machining path of the mounting surface of external attachments on remanufactured engine blocks. Utilizing 3D scanning, the cylinder block model is acquired, and machining coordinates are reverse processed. The tool path problem is then converted into a Traveling Salesman Problem (TSP). The shortcomings of the ant colony algorithm are addressed by incorporating dynamically selected pheromone update rules and a global update strategy, enhancing its global search capability. The max-min ant system is introduced to elevate convergence speed, while boosting pheromone concentration on critical processing areas. Simulation and experimental results demonstrate a 0.18% reduction in path length compared to traditional ACO algorithms, with a remarkable decrease in iterations by 91.67% and iteration time by 52.79%. This experimentation validates the efficacy of the enhanced ant colony algorithm, significantly reducing machining time and enhancing the efficiency and quality of remanufactured cylinder block machining.

关键词： Engine block external attachment mounting surface improved ant colony algorithm Path optimization Pheromone update Maximum minimum ant colony system

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Cooperative Search and Rescue Target Assignment Based on improved ant colony algorithm 15th

Cooperative Search and Rescue Target Assignment Based on Imp...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Zhu, Defa Yang, Lichun Wu, Jiaqi Jiangsu Automat Res Inst Lianyungang 222006 Jiangsu Peoples R China

ISBN: (纸本)9789819771806;9789819771813

When a large number of people fall into the water at sea, the key to the success of maritime search and rescue is to quickly arrive at these locations and finish the rescue task. A target assignment model of multiple resources cooperative search and rescue at sea is established, and a target assignment solution based on improved ant colony algorithm is proposed. This method optimizes the target selection mechanism of search and rescue resources, and makes full use of the global search ability of ant colony algorithm to explore the optimal solution. The simulation results show that this method can quickly and effectively provide the target assignment scheme of search and rescue resources, maximize the survival probability, and improve the efficiency of search and rescue at sea.

关键词： Cooperative Search and Rescue improved ant colony algorithm Target Assignment

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