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Optimization of adaptive Active Support Parameters for Large Deformation Prestressed Anchor Ropes in Soft Rock Tunnels Using genetic algorithm Based Computer Applications

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Procedia Computer Science 2024年 243卷 496-505页

作者： Shanshan Wu Ning Zhang Jicheng Dong Li Chen School of Railway and Architecture Chongqing Vocational College of Public Transportation 402247 Chongqing China Huazhi (Chongqing) Engineering Technology Co. 400713 Chongqing China

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple genetic algorithm and Parallel genetic algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward.

关键词： Soft Rock Tunnel Prestressing Anchor Cables adaptive genetic algorithm Parameter Optimization

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Improved self-adaptive genetic algorithm with quantum scheme for electromagnetic optimisation

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IET MICROWAVES ANTENNAS & PROPAGATION 2014年第12期8卷 965-972页

作者： Wei, Xiao-Kun Shao, Wei Zhang, Cheng Li, Jia-Lin Wang, Bing-Zhong Univ Elect Sci & Technol China Sch Phys Elect Chengdu 610054 Peoples R China

In this study, an accurate and efficient quantum genetic algorithm (QGA) combined with an improved self-adaptive (SA) scheme is proposed to solve electromagnetic optimisation problems. QGA is employed as the main optimisation frame because of its wider search range and higher efficiency than the conventional genetic algorithm. By introducing an improved SA scheme, the population at each generation is divided into two groups for crossover operation according to the magnitudes of individual fitness values. The crossover probability and mutation rate remain unchanged at the early stage of iterative process while the SA scheme will be carried out for the rest of the iterative process. Moreover, the elitist model is introduced to save the optimal father-individuals and abandon the worst ones. All these strategies make the whole population nearly converge to the optimal solution very fast. In two numerical examples of filter design and linear array synthesis, the effectiveness of the author's proposed optimisation algorithm, combined with the finite-difference time-domain method and finite-element method in HFSS, respectively, is verified.

关键词： Scheme Iterative Process Range searching self-adaptive adaptive genetic algorithm Finite element method filter design Mutation Rate Optimization algorithms Finite difference time domain method ELECTROMAGNETIC genetic algorithm (GA)

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Application of LSTM model optimized by individual-ordering- basedadaptivegeneticalgorithmin stock forecasting

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International Journal of Intelligent Computing and Cybernetics 2023年第2期16卷 277-294页

作者： Yong He Xiaohua Zeng Huan Li Wenhong Wei School of Computer Dongguan University of TechnologyDongguanChina School of Economics and Trade Guangzhou Xinhua UniversityDongguanChina

Purpose-To improve the accuracy of stock price trend prediction in the field of quantitative financial trading,this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory(LSTM).Design/methodology/approach-In this paper,an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network ***-The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning ***,the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization ***/value-(1)The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter *** with other models,it has higher accuracy in predicting the up and down trend of stock prices in the next day.(2)Adopting real coding,elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm,the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

关键词： Stock price prediction Long-short-term memory adaptive genetic algorithm Machine learning

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Task Allocation Optimization in Collaborative Customized Product Development Based on adaptive genetic algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2014年第1期23卷 1-19页

作者： Bao, Beifang Yang, Yu Liu, Aijun Zhao, Jiali Li, Leiting Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China Xidian Univ Econ & Management Sch Xian 710071 Peoples R China Chongqing Acad Sci & Technol Chongqing 401123 Peoples R China

Due to the currently insufficient consideration of task fitness and task coordination for task allocation in collaborative customized product development, this research was conducted based on the analysis of collaborative customized product development process and task allocation strategy. The definitions and calculation formulas of task fitness and task coordination efficiency were derived, and a multiobjective optimization model of product customization task allocation was constructed. A solution based on adaptive genetic algorithm was proposed, and the feasibility and effectiveness of the task allocation algorithm were tested and verified using a 5-MW wind turbine product development project as example.

关键词： Collaborative customized product development task allocation task fitness task coordination efficiency adaptive genetic algorithm

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The Application of the Linear adaptive genetic algorithm to Optimal Power Flow Problem

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第6期39卷 4901-4909页

作者： Abusorrah, Abdullah M. King Abdulaziz Univ Fac Engn Renewable Energy Res Grp Jeddah 21413 Saudi Arabia King Abdulaziz Univ Fac Engn Elect & Comp Engn Dept Jeddah 21413 Saudi Arabia

This paper proposes a Linear adaptive genetic algorithm (LAGA) for optimal power flow (OPF) problem. The proposed approach offers faster convergence than the standard genetic algorithm. In this study, LAGA is applied to the 6-bus system and the IEEE 14-bus power system. Shunt capacitance, transformer taps and generator voltages are used as control system variables to minimize the system power loss. The output of the systems under investigation is compared with the output of a classical nonlinear optimization routine to evaluate the impact of LAGA technique to OPF. Moreover, to validate the performance of LAGA approach, a demonstration and comparison with earlier published results is presented. Simulation results are found to be effective and promising.

关键词： Optimal power flow Reactive power control adaptive genetic algorithm Optimization

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Emergency dynamic alliance partner selection based on adaptive genetic algorithm 3

Emergency dynamic alliance partner selection based on adapti...

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3rd Asian Pacific Conference on Mechanical Components and Control Engineering (ICMCCE)

作者： Hu, Min Beijing Informat Sci & Technol Univ Sch Informat Management Beijing Peoples R China

ISBN: (纸本)9783038353126

Emergency mobilization alliance partner selection process is longitudinally choice of the value chain to achieve a task. Value of subtasks coefficient has been discussed. Depending on the difficulty of the problem and analytical perspective, the model of emergency mobilization alliance partner selection is given to maximize the overall effectiveness of the emergency mobilization. The choice of partner selection using adaptive genetic algorithm is made and the comparison with other methods has been analyzed.

关键词： emergency dynamic alliance adaptive genetic algorithm partner selection

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An adaptive genetic algorithm for the time dependent inventory routing problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2014年第5期25卷 1025-1042页

作者： Cho, Dong Won Lee, Young Hae Lee, Tae Youn Gen, Mitsuo Hanyang Univ Dept Ind & Management Engn Ansan 426791 Gyeonggi Do South Korea Fuzzy Log Syst Inst Iizuka Fukuoka Japan

In this paper we propose an adaptive genetic algorithm that produces good quality solutions to the time dependent inventory routing problem (TDIRP) in which inventory control and time dependent vehicle routing decisions for a set of retailers are made simultaneously over a specific planning horizon. This work is motivated by the effect of dynamic traffic conditions in an urban context and the resulting inventory and transportation costs. We provide a mixed integer programming formulation for TDIRP. Since finding the optimal solutions for TDIRP is a NP-hard problem, an adaptive genetic algorithm is applied. We develop new genetic representation and design suitable crossover and mutation operators for the improvement phase. We use adaptive genetic operator proposed by Yun and Gen (Fuzzy Optim Decis Mak 2(2):161-175, 2003) for the automatic setting of the genetic parameter values. The comparison of results shows the significance of the designed AGA and demonstrates the capability of reaching solutions within 0.5 % of the optimum on sets of test problems.

关键词： Time dependent inventory routing problem adaptive genetic algorithm Mixed integer programming

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Research on the Sensor Coarse Signal Processing Model Based on adaptive genetic algorithm

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3rd International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation(CDMMS 2013)

作者： Zou Donglan XinYuCollege Math & Comp Sci Inst Xinyu Jiangxi Peoples R China

ISBN: (纸本)9783037859100

With the rapid development of electronic information science and network transmission technology, the signal processing technology has been widely applied to various fields, which is the most important component of signal detection and transmission, and the key signal processing technology for processing sensor crude signals. Based on this, the experimental system of sensor coarse signal processing model is established, and in the experimental system, the transformer can carry out signal recognition for voltage and current, the use of PC microcontroller and embedded AD converter carries out analog / digital conversion for sensor crude signal. For the amplification process of sensor coarse signal, the use of adaptive genetic algorithm carries out mathematical modeling, the realization of the signal identification, acquisition and processing functions through software programming control. Finally, the intelligent processing of sensor coarse signal is successfully completed by the experiment system, and the signal processing effect is given as well.

关键词： adaptive genetic algorithm Sensor Coarse signal processing PC microcontroller Embedded software AD converter

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Clustering Analysis based on adaptive genetic algorithm for Performance Assessment

Clustering Analysis based on Adaptive Genetic Algorithm for ...

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26th Chinese Control and Decision Conference (CCDC)

作者： Gao Xiangpeng Wang Jianhua Liang Shan Shenyang Artillery Acad Training Dept Shenyang 110867 Peoples R China Shenyang Cultural Broadcasting Press & Publicat B Shenyang 110002 Peoples R China Gen Armament Dept 119 Mil Representat Off Beijing 110000 Peoples R China

ISBN: (纸本)9781479937066

This paper analyses and studies genetic algorithm and classical clustering algorithms, and then the demand analysis and design of the personnel management system of Shenyang Administration College. The adaptive crossover probability and adaptive mutation probability are proposed, which consider the influence of every generation to algorithm and the effect of different individual fitness in every generation. Theory and experiment shows that the algorithm can concluded some results of having meaning practically to guide college personnel management.

关键词： adaptive genetic algorithm cluster analysis performance assessment

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Burden Distribution Control Based on adaptive genetic algorithm in Bell-less Blast Furnace 33

Burden Distribution Control Based on Adaptive Genetic Algori...

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33rd Chinese Control Conference (CCC)

作者： Zhu, Qiao Yin, Yi-Xin Cheng, Han-Qing Chen, Xian-Zhong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Minist Educ Key Lab Adv Control Iron & Steel Proc Beijing 100083 Peoples R China

ISBN: (纸本)9789881563842

The proper control of the ratio of ore to coke distribution (ROCD) can achieve energy saving of the blast furnace (BF). In order to achieve the desired ROCD, it is meaningful to search best charging system (especially the burden matrix). This paper deals with the problem of burden distribution control based on the adaptive genetic algorithm and multi-radar data. Firstly, the definition of the ratio of ore to coke (ROC) and the desired ROCD are given. Secondly, according to the desired ROCD, the concrete steps of searching the best burden matrices are presented based on the adaptive genetic algorithm. Then, the operators of the adaptive genetic algorithm (especially the mutation operator) are properly adjusted, according to the actual production process of the bell-less BF. Finally, three computational experiments demonstrate the effectiveness of the method.

关键词： Bell-less blast furnace adaptive genetic algorithm Ratio of ore to coke distribution Multi-loop burden matrices

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