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Multi-ant colony algorithm based on cooperative game and dynamic path tracking

COMPUTER NETWORKS 2023年 237卷

作者： Wu, Lisheng You, Xiaoming 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

For the problems of slow convergence and low stability in traditional ant colony algorithm when solving largescale Traveling Salesman Problem (TSP), a multi-ant colony algorithm based on cooperative game and dynamic path tracking (CDMACA) is proposed. Firstly, a novel heterogeneous multi-population is formed by ant colony System (ACS) and Max-Min ant System (MMAS). Secondly, a cooperative game is introduced. In the isomorphic populations, all the populations cooperate and then the pheromone income will be distributed to the participants based on the contribution. Populations with higher contribution will obtain more income, which can improve the accuracy of the solutions;On the other hand, in the heterogeneous populations, a learning mechanism is introduced to enable heterogeneous populations to learn the optimal solution from each other to further improve the accuracy. Thirdly, a dynamic path tracking mechanism is proposed to reward or punish the public paths with pheromone based on the similarity of the optimal solutions, which can enhance the concentration of pheromone on the optimal path, thus improving the convergence. Finally, when the algorithm stalls, a pheromone balance mechanism is introduced. By dynamically cutting the pheromone on the current optimal path, the probability of the optimal path being chosen will decrease, which can help the algorithm jump out of the local optimum. Through a large number of TSP instances, the proposed algorithm in this paper can further improve the accuracy when solving large-scale TSP compared with other improved algorithms.

关键词： ant colony algorithm Cooperative game Dynamic path tracking mechanism Learning mechanism Pheromone balance mechanism

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An equilibrium analysis of the impact of real estate price volatility on macroeconomics based on ant colony algorithm

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2023年第2期45卷 70-70页

作者： Jie, Zhou Qi, Chai Hua Northwestern Polytech Univ Sch Management Xian 710072 Shaanxi Peoples R China

In view of the large subjective arbitrariness of traditional real estate appraisal methods, through analysis of the advantages and disadvantages of the least square support vector machine (LS-SVM) model for real estate appraisal, the use of ant colony algorithm is proposed for the parameter selection problem (ACO) is optimized, and a real estate valuation model based on ant colony algorithm optimization least square support vector machine (ACO-LS-SVM) is established after integration. On this basis, in order to study the impact of real estate price fluctuations on the macroeconomic equilibrium, this paper studies the impact of real estate price fluctuations on my country's macroeconomic and sectoral economies by constructing a computable general equilibrium model. The main findings are as follows: the output of various industries and the direction of changes in real estate prices are consistent, but industries such as heavy industry, extractive industries, and real estate are relatively affected;the rise and fall of real estate prices have caused the income of all residents to decline, but each income The impacts on urban residents of different ranks are not the same, and the incomes of the government and enterprises are in the same direction as the changes in real estate prices. The efficiency of the predicted price of real estate appraisal technique is 4.08% and the average relative error is 1.78%. On the whole, the increase in real estate prices has a greater driving effect on economic growth, but the negative impact of a price drop of the same magnitude is greater.

关键词： ant colony algorithm Least squares support vector machine Real estate price prediction Real estate price fluctuation Macroeconomic impact

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Data-driven allocation of smart grid-connected system based on ant colony optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 6795-6805页

作者： Liu, Qi Dongguan City Univ Dongguan Guangdong Peoples R China Univ Utara Malaysia Sintok Kedah Malaysia

In the era of advanced technology, integrating and distributing data are crucial in smart grid-connected systems. However, as energy loads continue to increase, practical implementation of these systems faces challenges in resource allocation and lacks efficient data collaboration. In this study, the ant colony optimization algorithm is further investigated for stochastic crossover systems and cluster nodes in intelligent path planning management. To improve the pheromone setting method in smart grid-connected systems, we propose an adaptive intelligent ant colony optimization algorithm called the Group Allocation Optimization algorithm (GAOA). This algorithm expands the pheromone transmission rate of network nodes, establishes a multi-constrained adaptive model with data mining as the pheromone target, and analyzes the accuracy of resource allocation to import the optimal scheme for smart grid-connected systems. Through experimental results, we demonstrate that the optimized adaptive ant colony algorithm leads to effective improvements in grid-connected systems, pheromone evaluation, data throughput, convergence speed, and data load distribution. These findings provide evidence that the optimized ant colony algorithm is both feasible and effective for resource allocation in smart grid-connected systems.

关键词： Smart Grid-connected system data-driven allocation ant colony algorithm group allocation optimization algorithm

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Optimization of Cold Chain Logistics Vehicle Transportation and Distribution Model Based on Improved ant colony algorithm

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Procedia Computer Science 2023年 228卷 974-982页

作者： Xiaoman Guan Guangpeng Li Shandong Institute of Commerce and Technology Jinan 250103 Shandong China

Cold chain logistics(CCL) plays an increasingly important role in modern logistics. In order to achieve efficient and high-quality CCL, providing feasible methods in vehicle transportation and distribution(VCD) is crucial. This article adopts an improved ant colony algorithm(ACA), with transportation cost and time as the main objectives, to optimize common path selection problems in VCD processes, and proves the effectiveness of this algorithm through experiments. The improved ACA is introduced to enhance the search ability, and the pheromone is constantly updated in the search process to find a better path. The experimental results show that the total cost of CCL transportation for agricultural products obtained by improving the ACA is 821 yuan, and customer satisfaction reaches 100%. The advantage of this algorithm is that it can comprehensively consider various factors, search for global optimal solutions, adapt to various complex scenarios, and is not easily trapped in local optima.

关键词： Cold Chain Logistics ant colony algorithm Distribution Model Path Selection

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Hybrid genetic ant colony optimization algorithm for full-coverage path planning of gardening pruning robots

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INTELLIGENT SERVICE ROBOTICS 2024年第3期17卷 661-683页

作者： Xie, Xiaolin Yan, Zixiang Zhang, Zhihong Qin, Yibo Jin, Hang Xu, Man Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471003 Henan Peoples R China Longmen Lab Luoyang 471003 Henan Peoples R China

Gardening pruning robots are widely applied in green space construction. However, increase of green space environment complexity and obstacle number affect the coverage range and work efficiency of robots. To solve this problem, this research proposed a full-coverage path planning algorithm integrating hybrid genetic ant colony and A* algorithm. Specifically tailored to the lawn working environments of horticultural pruning robots, we initially employed visual simultaneous localization and mapping to create a 3D point cloud map, converting it into an occupancy grid map for future path planning. The obtained grid map was partitioned into multiple subareas on the basis of the locations of obstacles. The optimal traversal order of sub-regions was determined using hybrid genetic ant colony method and a new update strategy of heuristic and pheromone factors was developed for improving the ability of global search and probability of jumping out of local optimal solution. Boustrophedon method was applied to fully cover each sub-region, A* algorithm was adopted to connect various sub-regions, and connection strategy was optimized. Simulation results showed that compared with traditional ant colony algorithm and other full-coverage planning algorithms, the algorithm developed in this research presented superior performance in terms of traversal path length, starting distance, coverage rate and turning times on maps with various sizes and complexities.

关键词： Agricultural robot Full-coverage path planning ant colony algorithm Genetic algorithm A*algorithm Travelling salesman problem

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An AGV Cluster Task Scheduling Method Based on Improved ant colony algorithm

An AGV Cluster Task Scheduling Method Based on Improved Ant ...

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第43届中国控制会议

作者： Junlin WANG Zhengwei ZHU School of Information Engineering Southwest University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the AGV cluster delivery task scheduling problem,an improved ant colony algorithm is proposed to solve the problem of slow convergence speed and poor path solution of traditional ant colony ***,the AGV cluster delivery task scheduling problem and its mapping relationship with ant colony algorithm are ***,the ant colony algorithm is improved by designing an ant colony death strategy to select the pheromone update range,designing an ant colony classification and pheromone superposition method to improve the pheromone update rule,and introducing an maximum and minimum ant colony system to limit the pheromone accumulation ***,the proposed algorithm is compared with other reference algorithms by using two validation cases,which verified the effectiveness and superiority of the proposed algorithm in solving the AGV cluster delivery task scheduling problem.

关键词： AGV Cluster delivery Task scheduling ant colony algorithm

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Application study of ant colony algorithm for network data transmission path scheduling optimization

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JOURNAL OF INTELLIGENT SYSTEMS 2023年第1期32卷

作者： Xiao, Peng Jiangxi Vocat Coll Ind & Engn Sch Informat Engn Pingxiang 337000 Peoples R China

With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within +/- 2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

关键词： ant colony algorithm heuristic function traffic scheduling link load pheromone balanced strategy SDN

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Multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3113-3133页

作者： Wu, Qihuan You, Xiaoming Liu, Sheng Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Univ Engn Sci Sch Management Shanghai 201620 Peoples R China

To solve the problems of slow convergence and insufficient accuracy of traditional ant colony algorithms in solving large-scale problems, this paper proposes a multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model (GHMACO). Firstly, the heterogeneous multi-ant colony model is constructed, and each colony collaborates to improve the performance of the algorithm. Secondly, in order to enhance the communication among the heterogeneous colonies, a non-cooperative game strategy is introduced. The heterogeneous ant colonies are divided into the propagating colony and the absorbing colonies, where the propagating colony propagates the optimal payoffs of the game, and the absorbing colonies choose optimal strategies for absorption to balance the convergence and diversity of the algorithm. Further, the hierarchical temporal memory model is adopted to perform hierarchical optimization strategies based on path memories which includes: local exploration strategy, pheromone redistribution strategy and path replacement strategy, thus improving the accuracy of the algorithm and helping the colonies to jump out of the local optimum. Experiments on the traveling salesman problem show that the improved algorithm is effective in improving the convergence and accuracy of the traditional ant colony algorithms, especially in large-scale problems.

关键词： ant colony algorithm Non-cooperation game Hierarchical temporal memory model Traveling salesman problem

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Research on Improved Potential Field ant colony algorithm for UAV Path Planning 33

Research on Improved Potential Field Ant Colony Algorithm fo...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Chen, Tao Lv, Xinyu Wang, Shengying Ta, Na Zhao, Jing Chen, Xinpei Xiao, Mingxia Wei, Haicheng North Minzu Univ Sch Elect & Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Univ Sch Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Presch Educ Coll Educ Informat Ctr Yinchuan 750001 Ningxia Peoples R China

ISBN: (纸本)9781665440899

In order to solve the problems of slow convergence speed and prone to local optimization in ant colony algorithm, an improved ant colony algorithm of potential field is proposed in this study. Firstly, the method of pheromone affected by artificial potential field is introduced to reduce the blindness of the ant colony. Then, the potential field heuristic information is used to accelerate the convergence speed of ant colony algorithm. Finally, the decrease coefficient of potential field resultant force is used to solve the local optimal problem. Compare with the traditional algorithm, the experiments show that the improved algorithm in this paper can get better results, in terms of path length and the number of convergence iterations, the average search time for convergence is reduced by 44.83% in the 20x20 map.

关键词： path planning ant colony algorithm artificial potential field algorithm the decrease coefficient of potential field resultant force

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The Optimisation of Communication in Power Carrier Sensor Control Networks Using the ant colony algorithm 23

The Optimisation of Communication in Power Carrier Sensor Co...

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Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering

作者： Jie Hou China Aviation Planning and Design Institute (Group) CO. LTD. China

ISBN: (纸本)9798400708299

The aim of this study is to optimise power carrier sensor control network communication through the application of ant colony algorithm in order to improve energy utilisation efficiency and network performance. Power carrier sensor networks have a wide range of applications in real power system monitoring and control, however, they have limited energy sources, restricted communication distances, and are susceptible to local optimal solutions. To solve these problems, we propose an optimisation method based on ant colony algorithm. In this study, we first analysed the characteristics and communication requirements of power carrier sensor networks in detail. Then, we designed an ant colony algorithm, which includes a mechanism to dynamically adjust the parameters (e.g., , , and ) to adapt to different stages of communication requirements. The algorithm also considers factors such as node energy consumption and residual energy to achieve energy-aware awareness and avoid energy imbalance problems. Through simulation experiments, we verify the superiority of the proposed algorithm in terms of network communication performance. The results show that the improved algorithm has significant advantages over traditional methods in reducing node energy consumption, decreasing communication distance, improving network coverage and avoiding local optimal solutions. In conclusion, this study provides an effective optimisation method for power carrier sensor-controlled network communication, which can achieve more efficient energy utilisation and network performance enhancement through the application of ant colony

关键词： ant colony algorithm Communication Optimisation Energy Management Network Performance Power Carrier Sensor Networks

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