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Research on UAV cloud control system based on ant colony algorithm

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Journal of Systems Engineering and Electronics 2022年第4期33卷 805-811页

作者： ZHANG Lanyong ZHANG Ruixuan School of Intelligent Science and Engineering Harbin Engineering UniversityHarbin 150001China

In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the difficulty of data processing in the cloud era, it is extremely important to perform massive data operations through cloud servers. Unmanned aeriel vehicle(UAV) control is the representative of the intelligent field. Based on the ant colony algorithm and incorporating the potential field method, an improved potential field ant colony algorithm is designed. To deal with the path planning problem of UAVs, the potential field ant colony algorithm shortens the optimal path distance by 6.7%, increases the algorithm running time by39.3%, and increases the maximum distance by 24.1% compared with the previous improvement. The cloud server is used to process the path problem of the UAV and feedback the calculation results in real time. Simulation experiments verify the effectiveness of the new algorithm in the cloud environment.

关键词： ant colony algorithm potential field method cloud server path planning

Optimizing Fresh Logistics Distribution Route Based on Improved ant colony algorithm

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Computers, Materials & Continua 2022年第10期73卷 2079-2095页

作者： Daqing Wu Ziwei Zhu Dong Hu Romany Fouad Mansour College of Economics and Management Shanghai Ocean UniversityShanghai201306China Nanchang Institute of Technology Nanchang330044China Shanghai Jianqiao College Shanghai201306China Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Karaga72511Egypt

With the rapid development of the fresh cold chain logistics distribution and the prevalence of low carbon concept,this paper proposed an optimization model of low carbon fresh cold chain logistics distribution route considering customer satisfaction,and combined with time,space,weight,distribution rules and other constraints to optimize the distribution model.At the same time,transportation cost,penalty cost,overloading cost,carbon tax cost and customer satisfaction were considered as the components of the objective function,and the thought of cost efficiency was taken into account,so as to establish a distribution model based on the ratio of minimum total cost to maximum satisfaction as the objective function.Then,the improved A∗algorithm and ant colony algorithm were used to construct the model solution.Through the simulation analysis results of different calculation examples,the effectiveness,efficiency and correctness of the design of the single target low-carbon fresh agricultural products cold chain model by using the improved ant colony algorithm were verified.

关键词： Carbon tax cost vehicle routing problem cold chain ant colony algorithm

A Drone-Based Blood Donation Approach Using an ant colony Optimization algorithm

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1917-1930页

作者： Sana Abbas Faraha Ashraf Fahd Jarad Muhammad Shoaib Sardar Imran Siddique School of Mathematics Minhaj UniversityLahore54770Pakistan Department of Mathematics Cankaya UniversityEtimesgutAnkara06790Turkey Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung40402Taiwan Department of Mathematics University of Management and TechnologyLahore54770Pakistan

This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.

关键词： Network ant colony algorithm path complete graph blood banks drones travelling salesman problem

ant colony algorithm and Optimization of Test Conditions in Analytical Chemistry

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Chinese Journal of Chemistry 2003年第6期21卷 607-609页

作者：丁亚平吴庆生苏庆德 Department of Chemistry University of Science and Technology of China Hefei Anhui 230026 China Department of Chemistry Shanghai University Shanghai 200436 China Department of Chemistry Tongji University Shanghai 200092 China Department of Chemistry University of Science and Technology of China Hefei Anhui 230026 China

The research for the new algorithm is in the forward position and an issue ofgeneral interest in chemometrics all along. A novel chemometrics method, Chemical ant colonyalgorithm, has first been developed. In this paper, the basic principle, the evaluation function,and the parameter choice were discussed. This method has been successfully applied to the fitting ofnonlinear multivariate function and the optimization of test conditions in chrome-azure-S-Alspectrophotometric system. The sum of residual square of the results is 0.0009, which has reached agood convergence result.

关键词： ant colony algorithm optimization fitting CASA1 photometry

ant colony algorithm for Solving QoS Routing Problem

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Wuhan University Journal of Natural Sciences 2004年第4期9卷 449-453页

作者： SUNLi-iuan WANGLiang-jun WANGRu-chuan DepartmentofComputerScienceandTechnology NanjingUniversityofPostsandTelecommunicationsNanjing210003JiangsuChina

Based on the state transition rule, the local updating rule and the global updating rule of ant colony algorithm, we propose an improved ant colony algorithm of the least\|cost quality of service (QoS) unicast routing. The algorithm is used for solving the routing problem with delay, delay jitter, bandwidth, and packet loss\|constrained. In the simulation, about 52.33% ants find the successful QoS routing , and converge to the best. It is proved that the algorithm is efficient and effective.

关键词： ant colony algorithm QoS unicast routing

Path Planning for AUVs Based on Improved APF-AC algorithm

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Computers, Materials & Continua 2024年第3期78卷 3721-3741页

作者： Guojun Chen Danguo Cheng Wei Chen Xue Yang Tiezheng Guo Industrial Center Nanjing Institute of TechnologyNanjing211167China

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

关键词： Path-planning autonomous underwater vehicle ant colony algorithm artificial potential field bio-inspired neural network

Improved Multi-objective ant colony Optimization algorithm and Its Application in Complex Reasoning

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Chinese Journal of Mechanical Engineering 2013年第5期26卷 1031-1040页

作者： WANG Xinqing ZHAO Yang WANG Dong ZHU Huijie ZHANG Qing University of Science and Technology of Chinese People’s Liberation Army School of Mechanical Engineering Tianjin University

The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

关键词： fault reasoning ant colony algorithm Pareto set multi-objective optimization complex system

ant colony optimization algorithm and its application to Neuro-Fuzzy controller design

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Journal of Systems Engineering and Electronics 2007年第3期18卷 603-610页

作者： Zhao Baojiang Li Shiyong Dept. of Control Science and Engineering Harbin Inst. of Technology Harbin 150001 P.R.China Dept. of Mathematics Mudanjiang Teachers Coll. Mudanjiang 157012 P.R. China

An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.

关键词： neuro-fuzzy controller ant colony algorithm function optimization genetic algorithm inverted pen-dulum system.

Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm

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Chinese Journal of Aeronautics 2014年第3期27卷 678-687页

作者： Li Yuqing Wang Rixin Xu Minqiang Deep Space Exploration Research Center Harbin Institute of Technology

This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.

关键词： ant colony algorithm Planning and scheduling Rescheduling Spacecraft observing Uncertainty