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An improved ant colony optimization algorithm for two-sided U-type assembly line balancing problems

ENGINEERING optimization 2025年第5期57卷 1245-1262页

作者： Jiao, Yuling Su, Xinyue Li, Lin Wu, Zhouhua Jilin Univ Coll Transportat Changchun Peoples R China

Aiming at the improvement of production system, a new method of two-sided U-type assembly line balance is proposed. Define the partition of a two-sided U-type assembly line and derive the formula for the task start time. The multi-objective model of two-sided U-type assembly line balance is established by minimizing a smoothing index, the number of positions and stations. Considering the dual influence of heuristic factors and pheromone information, the improved ant colony optimization algorithm is proposed. Combined with 37 examples, the improved ant colony optimization algorithm is used to solve the balancing problem of a two-sided straight line and a two-sided U-type assembly line, respectively. Compared with the results of other algorithms, it verifies that the model is reasonable and the improved ant colony optimization algorithm is effective. The balance effect of a two-sided U-type assembly line is better than that of a two-sided straight line, which means the length of the assembly line is shortened.

关键词： Logistics engineering two-sided U-type assembly line balancing problem improved ant colony optimization algorithm collaborative optimization partition layout

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An improved ant colony optimization algorithm in mobile robot path planning 40

An improved ant colony optimization algorithm in mobile robo...

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40th Chinese Control Conference (CCC)

作者： Li, Hui Yang, Kang Luo, Wanbo Dong, Bo Qin, Wei Cong, Shuofeng Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China Acad Mil Med Sci Inst Mil Vet Zoonosis Prevent & Control Key Lab Changchun 130122 Peoples R China

ISBN: (纸本)9789881563804

In order to promote the convergence speed and global optimization effect, an improved ant colony optimization algorithm (IACO) is proposed. Firstly, an initial pheromone unequal rule is constructed, which is based on the starting and ending positions to avoid the blindness of early search. Secondly, the heuristic information function is improved by considering the distance between the starting position and the target position. Thirdly, the state transition pseudo random strategy is utilized to select the next node and the state transition probability coefficient is adaptively adjusted. Finally, the pheromone update rules have been revised, that is, the pheromone volatilization coefficient can be dynamically adjusted and the pheromone threshold can also be set. Simulation results show that compared with traditional ant colony method, the proposed algorithm is valid and effective to settle path planning problems of mobile robot in different contraint environments.

关键词： improved ant colony optimization algorithm initial pheromone unequal rule heuristic information function the pheromone update rules path planning

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Punching trajectory optimization method for warp-knitted vamp based on improved ant colony optimization algorithm and Radau pseudospectral method

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2021年第16期16卷

作者： Chi Xinfu Li Qiyang Zhang Xiaowei Sun Yize Donghua Univ Coll Mech Engn Renmin North Rd 2999 Shanghai 201620 Peoples R China

Aiming at the problems of complex trajectory, low efficiency and high operational difficulty of the robot in multi-point punching of warp-knitted vamp, a method of optimizing punching trajectory based on improved ant colony optimization algorithm and Radau pseudospectral method is proposed. After obtaining the position coordinates of punching points, an improved ant colony optimization algorithm is used to calculate the punching sequence of the shortest path through all punching points, and then Radau pseudospectral method is used to solve the optimal trajectory of the laser punching robot. improved ant colony optimization algorithm combines a distributed calculation method and the positive feedback mechanism. Radau pseudospectral method can transform the optimal control problems into nonlinear programming problems, and the combination of the two can quickly and reliably obtain the optimal solution. To verify the method, under the condition of selecting the same number and location of punching points, the experiments of Radau pseudospectral method to solve the trajectory planning of laser punching robot is carried out. The experimental results show that improved ant colony optimization algorithm can calculate the path of the vamp punching point in a shorter time and with high accuracy. Radau pseudospectral method can obtain smooth trajectories satisfying various constraints, which can meet the requirements of accuracy and efficiency in practical production.

关键词： Warp-knitted vamp punching motion motion planning improved ant colony optimization algorithm Radau pseudospectral method

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Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and improved ant colony optimization algorithm

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ENERGIES 2018年第8期11卷 2106页

作者： Hu, Bingtao Feng, Yixiong Zheng, Hao Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China

With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

关键词： energy consumption selective disassembly disassembly sequence planning constraints relation graph improved ant colony optimization algorithm

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An improved ant colony optimization algorithm in mobile robot path planning

An improved ant colony optimization algorithm in mobile robo...

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第40届中国控制会议

作者： Hui Li Kang Yang Wanbo Luo Bo Dong Wei Qin Shuofeng Cong Department of Control Science and Engineering Changchun University of Technology Institute of Military Veterinary Academy of Military Medical Sciences Zoonosis Prevention and Control Key Laboratory

In order to promote the convergence speed and global optimization effect, an improved ant colony optimization algorithm（IACO） is proposed. Firstly, an initial pheromone unequal rule is constructed, which is based on the starting and ending positions to avoid the blindness of early search. Secondly, the heuristic information function is improved by considering the distance between the starting position and the target position. Thirdly, the state transition pseudo random strategy is utilized to select the next node and the state transition probability coefficient is adaptively adjusted. Finally, the pheromone update rules have been revised, that is, the pheromone volatilization coefficient can be dynamically adjusted and the pheromone threshold can also be set. Simulation results show that compared with traditional ant colony method, the proposed algorithm is valid and effective to settle path planning problems of mobile robot in different contraint environments.

关键词： improved ant colony optimization algorithm initial pheromone unequal rule heuristic information function the pheromone update rules path planning

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Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach

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JOURNAL OF ENGINEERING-JOE 2023年第10期2023卷

作者： Wang, Wei Zhang, Jianfei Wang, Sai Chen, Xuewei State Grid Jibei Elect Power Co Ltd Tangshan Power Supply Co Tangshan Peoples R China Tangshan Power Supply Co State Grid Jibei Elect Po Tangshan 063000 Peoples R China

The rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision-making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations. This algorithm integrates improved ant colony optimization (IACO) algorithm and back propagation neural network (BPNN), which can effectively handle abnormal information, accurately map faults, and quickly locate fault modules. By integrating the IACO and BPNN in this manner, the proposed model effectively combines the global optimization ability of the IACO and the strong learning ability of the ***

关键词： fault location improved ant colony optimization algorithm intelligent substation neural network secondary equipment

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Forecast of flood disaster emergency material demand based on IACO-BP algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第5期34卷 3537-3549页

作者： Chen, Fujiang Chen, Junying Liu, Jingang Xihua Univ Sch Emergency Sci Chengdu 610039 Sichuan Peoples R China China Railway Acad Co Ltd Chengdu 610032 Sichuan Peoples R China

The frequent occurrence of various sudden natural disasters in the world has caused heavy losses to human beings. It is very important to forecast the demand of emergency materials in order to protect people's safety and property. The purpose of this study is to use IACO-BP algorithm to forecast the demand of emergency supplies in the case of flood disaster. In this study, the flood disaster situation in recent years published by the Ministry of Water Resources of China is selected as the experimental data set. The data are sorted and analyzed by a variety of theoretical comprehensive operation, qualitative and quantitative research method, analytic hierarchy process and system dynamics method. The improved ant colony optimization algorithm is used to model the emergency material demand, and the population, flood level, flood level of the disaster area are analyzed. As the network input, the material situation outputs the material demand, so as to forecast. The results show that the iteration times of IACO-BP algorithm are 11 and the running time is 3S. The fluctuation of IACO-BP algorithm is the least and the most stable among the three algorithms. The material satisfaction degree predicted by IACO-BP algorithm is improved by 15% from the original 80.9%. It is concluded that this algorithm is very accurate and efficient in the prediction of emergency material demand, which can better assist the disaster situation. This study contributes to the prediction of emergency material demand for emergency disaster.

关键词： improved ant colony optimization algorithm BP neural network Flood disaster Emergency supplies Demand forecasting

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Study on an airport gate assignment method based on improved ACO algorithm

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KYBERNETES 2018年第1期47卷 20-43页

作者： Deng, Wu Sun, Meng Zhao, Huimin Li, Bo Wang, Chunxiao Dalian Jiaotong Univ Software Inst Dalian Peoples R China Sichuan Univ Sci & Engn Sichuan Prov Key Lab Proc Equipment & Control Zigong Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning Peoples R China Dalian Jiaotong Univ Liaoning Key Lab Welding & Reliabil Rail Transpor Dalian Peoples R China Dalian Jiaotong Univ Dalian Key Lab Welded Struct & Its Intelligent Mf Dalian Peoples R China

Purpose - This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement. Design/methodology/approach - In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period. Findings - In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed. Practical implications - The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a verymeaningful work for airport gate assignment. Originality/value - An efficient multi-objective optimization

关键词： Robust optimization Performance analysis Airport gate assignment improved ant colony optimization algorithm Multi-objective optimization model

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Study on A Fault Diagnosis Method of Rolling Element Bearing Based on improved ACO and SVM Model

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2016年第3期9卷 167-180页

作者： Deng, Wu Li, Xiumei Zhao, Huimin Dalian Jiaotong Univ Software Inst Dalian 116028 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Southwest Jiaotong Univ Tract Power State Key Lab Chengdu 610031 Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China

The vibration signal is nonstationary and it is difficult to acquire the sample with typical fault. An improved ACO algorithm based on adaptive control parameters is introduced into SVM model to propose a new fault diagnosis (IMASFD) method in this paper. In the IMASFD method, the EMD method is used to decompose fault vibration signal into IMF components, the energy of IMF components is selected to construct the fault feature vectors. Then the adaptive controlling pheromone strategy, adaptive controlling stochastic selection threshold strategy and dynamic evaporation rate strategy are used to improve the basic ACO algorithm. The improved ACO algorithm is used to optimize the parameters of SVM model in order to obtain the optimal values of parameter combination in the SVM model. And a new fault diagnosis (IMASFD) method is proposed. Finally, the proposed IMASFD method is applied to the test data from bearing data center of CWRU. The experimental results show that the proposed method can accurately and effectively realize high precision fault diagnosis of rolling bearing, and has strong robustness and generalization ability, provides an effective method for realizing fault diagnosis of rolling bearing.

关键词： Fault diagnosis improved ant colony optimization algorithm support vector machine parameter optimization rolling bearing

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Modeling and simulation of lean supply chain with the consideration of delivery consolidation

Modeling and simulation of lean supply chain with the consid...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Miao Zhou Xu Kelin Tongji Univ Coll Mech Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9783037850176

An integrated production-inventory-delivery lean supply chain model is discussed. The model is integrated with production, supply and distribution through cost. And the delivery of purchasing and distribution is consolidated. A non-linear programming model with multivariable is proposed. The length of path and the frequency of delivery are regarded as decision-making variables. The optimal delivery path and the length of path are first solved by using improved ant colony optimization algorithm. And the optimal frequency of delivery and the batch of delivery are achieved. Last, the model is validated through an example and the result indicates the model is simple and practical. Moreover it is meaningful for practice.

关键词： lean supply chain modeling delivery consolidation improved ant colony optimization algorithm optimal delivery path optimal delivery batch

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