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improved adaptive genetic algorithm for dynamic multi-specification one-dimensional cutting problem

TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2024年第3期48卷 1-16页

作者： Liu, Li Liu, Zijin Qian, Xuefei Shenyang Jianzhu Univ Sch Mech Engn Shenyang 110168 Peoples R China North China Inst Aerosp Engn Sch Elect & Control Engn Langfang 065000 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China China Petr Pipeline Bur Engn Co Ltd Langfang 065000 Peoples R China

Rebar is an essential material in the construction of bridges and houses. Rebar cutting is an important link in rebar processing, but it is usually completed by manual experience, which is not only time-consuming, but also causes serious waste and reduces the economic benefits. As the country vigorously promotes the green construction method, the traditional rebar cutting method is difficult to meet the development requirements. So dynamic multi-specification one-dimensional cutting problem is studied in this paper. A mathematical model aiming at the maximum utilization rate of raw material is established, and an improved adaptive genetic algorithm is proposed. Large-scale, small-scale, single-specification masterbatch and multi- specification masterbatch are selected for simulation experiments, respectively. The results show that the proposed algorithm can deal with both large- and small-scale multi-specification or single-specification masterbatch cutting problems. Moreover, the algorithm has good performance in solving accuracy and convergence speed, which verifies its feasibility, effectiveness, and stability. Finally, aiming at the problem of dynamic insertion of orders, one-dimensional cutting software is developed, rapid and real-time cutting of rebars is realized, and the utilization rate of building rebars is improved, which plays a positive role in promoting the high-quality development of construction industries such as bridges.

关键词： one-dimensional cutting improved adaptive genetic algorithm multi-specification dynamic insertion

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Method for Fault Feature Selection for a Baler Gearbox Based on an improved adaptive genetic algorithm

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Chinese Journal of Mechanical Engineering 2022年第3期35卷 312-323页

作者： Bin Ren Dong Bai Zhanpu Xue Hu Xie Hao Zhang School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Yunnan Jingjian Rail Transit Investment&Construction Co. LtdKunming 650000China School of Mechanical Engineering Hebei University of Science and TechnologyShijiazhuang 050018China Nanjing Research Institute for Agricultural mechanization Nanjing 210014China

The performance and efficiency of a baler deteriorate as a result of gearbox *** way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring *** paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler *** method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute *** main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior ***,this method should be capable of timely diagnosis and *** validation was performed and promising findings highlighting the relationship between diagnosis results and faults were *** results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional *** proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.

关键词： Fault diagnosis Feature selection Attribute reduction improved adaptive genetic algorithm

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Determination the characteristic parameter of nano-film based on spectroscopic ellipsometry by improved adaptive genetic algorithm

Determination the characteristic parameter of nano-film base...

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Conference on AOPC - Optoelectronics and Nanophotonics

作者： Yue, Chong Ding, YueQing Tao, Lei Zhou, Sen Chen, Long Chongqing Acad Metrol & Qual Inspect Chongqing 401123 Peoples R China Chongqing Univ Coll Optoelect Engn Chongqing 400030 Peoples R China

ISBN: (纸本)9781510662261;9781510662278

In order to obtain accurate nano-film characteristic parameters in the ellipsometry measurement process, an optimization algorithm for solving the thickness and complex refractive index of nano films by spectroscopic ellipsometry is proposed. An improved adaptive genetic algorithm (IAGA) has been proposed to process nano-film data, this method combines the evolutionary algebraic attenuation factor with the adaptive genetic algorithm. It can solve the problem that the genetic algorithm is premature and easy to fall into the local optimization. The algorithm is used to calculate the film parameters of silicon dioxide nano film thickness standard template with standard value of 49.7 +/- 0.4 nm in this paper. The results show that the relative error of the calculation results of the film thickness is less than 3%, and the error of refractive index is less than 0.1. At the same time, it is verified by experiments that the IAGA algorithm model can effectively optimize the number of iterations, and has the advantages of fast convergence speed and high measurement efficiency.

关键词： ellipsometry measurement nano-films characteristic parameter improved adaptive genetic algorithm

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improved adaptive genetic algorithm for Course Scheduling in Colleges and Universities

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第6期13卷 29-42页

作者： Wang Wen-jing Shanxi Univ Business Coll Taiyuan Shanxi Peoples R China

Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.

关键词： course scheduling mathematical model improved adaptive genetic algorithm

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Optimal Trajectory Planning Based on improved adaptive genetic algorithm for a 6 DOF Manipulator 38

Optimal Trajectory Planning Based on Improved Adaptive Genet...

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38th Chinese Control Conference (CCC)

作者： Zhang, Haoyang Yuan, Yan Chen, Xin China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Hubei Peoples R China

ISBN: (纸本)9789881563972

This paper is concerned with the optimal trajectory planning for a 6 degree-of-freedom (DOF) manipulator, where the energy minimization is considered under the constraint of power. Firstly, based on the establishment of kinematics model, the influence of main factors on power is analyzed by using Jacobian matrix and Lagrange equation. Then the energy consumption is calculated as the objective function by the power consumption of each joint, and in order to obtain the desired trajectory for the arm, each joint locus is approximated by a 3-5-3 spline interpolation curve. To this end, in order to obtain the most reliable solutions for trajectory planning, the improved adaptive genetic algorithm (IAGA) is used to optimize the energy under the constraint of power, speed and acceleration, and the crossover rate and mutation rate are effectively adjusted with fitness. Finally, we conduct the simulation by using the dynamics analysis software-ADAMS. The results demonstrate the effectiveness of minimizing the optimized energy in the case of constrained power mainly.

关键词： Energy optimization trajectory planning improved adaptive genetic algorithm

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Fuzzy Energy Optimization Strategy of Parallel Hybrid Electric Vehicle Based on improved adaptive genetic algorithm 31

Fuzzy Energy Optimization Strategy of Parallel Hybrid Electr...

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31st Chinese Control And Decision Conference (CCDC)

作者： Sheng, Yang Wang, Xingcheng Liu, Mou Cai, Mingyu Dalian Maritime Univ Inst Marine Elect Engn Dalian 116026 Peoples R China

ISBN: (纸本)9781728101057

Parallel hybrid electric vehicle (PHEV) is a complex control system and in this paper we mainly study its energy control strategy. Firstly, a fuzzy logic control strategy is designed. The fuzzy controller takes the difference between the required torque and the optimal torque of the engine, state of charge (SOC) of the battery and the motor speed as the inputs, and the engine output torque factor as the output. Secondly, because the division of membership functions and the formulation of fuzzy rules have certain subjectivity, we use an improved adaptive genetic algorithm (IAGA) to optimize the parameters of the fuzzy controller. Finally, two control strategies are simulated on the ADVISOR platform. The simulation results show that compared with the original fuzzy control strategy, the optimized fuzzy logic control strategy could improve the fuel economy of the vehicle and reduce the emissions significantly.

关键词： Parallel Hybrid Electric Vehicle Energy control Strategy Fuzzy Control improved adaptive genetic algorithm

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Optimal Trajectory Planning Based on improved adaptive genetic algorithm for a 6 DOF Manipulator

Optimal Trajectory Planning Based on Improved Adaptive Genet...

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第三十八届中国控制会议

作者： Haoyang Zhang Yan Yuan Xin Chen School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the optimal trajectory planning for a 6 degree-of-freedom(DOF) manipulator, where the energy minimization is considered under the constraint of power. Firstly, based on the establishment of kinematics model, the influence of main factors on power is analyzed by using Jacobian matrix and Lagrange equation. Then the energy consumption is calculated as the objective function by the power consumption of each joint, and in order to obtain the desired trajectory for the arm, each joint locus is approximated by a 3-5-3 spline interpolation curve. To this end, in order to obtain the most reliable solutions for trajectory planning, the improved adaptive genetic algorithm(IAGA) is used to optimize the energy under the constraint of power, speed and acceleration, and the crossover rate and mutation rate are effectively adjusted with ***, we conduct the simulation by using the dynamics analysis software-ADAMS. The results demonstrate the effectiveness of minimizing the optimized energy in the case of constrained power mainly.

关键词： Energy optimization trajectory planning improved adaptive genetic algorithm

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Fault diagnosis of PV array based on optimised BP neural network by improved adaptive genetic algorithm

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The Journal of Engineering 2017年第13期2017卷 1427-1431页

作者： Yawei Liu Xiaorong Zhu Jin Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding People's Republic of China State Grid Hebei Electric Power Company Baoding Power Supply Company Baoding Heibei province Baoding Heibei province People's Republic of China

The fault diagnosis model based on back propagation (BP) neural network is especially suitable for multi-fault and complex pattern recognition. However, the selection of initial weights and thresholds of BP neural network is lack of basis and it is easy to fall into the local optimum. genetic algorithm (GA) can be used to optimise the initial weights and thresholds of the BP neural network. However, the selection process of GA based on roulette wheel selection is a random operation and the parameters of GA, the crossover probability and the mutation probability, are given a constant value which reduces the efficiency. Thus improved adaptive GA provides improvement in the selection, crossing and mutation process of GA, which prevents the neural network from falling into local optimum more effectively. Results of cases study show that this method can determine the fault types of photovoltaic array effectively.

关键词： backpropagation neural nets probability genetic algorithms fault diagnosis power engineering computing photovoltaic cells mutation process fault types PV array optimised BP neural network improved adaptive genetic algorithm fault diagnosis model back propagation neural network roulette wheel selection adaptive GA BP neural network multifault complex pattern recognition random operation crossing process photovoltaic array

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Optimal scheduling of water-supply pump stations based on improved adaptive genetic algorithm 35

Optimal scheduling of water-supply pump stations based on im...

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35th Chinese Control Conference (CCC)

作者： Zhu, Jiayu Wang, Jingcheng Li, Xiaocheng Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9789881563910

Suitable optimal scheduling model is essential to ensuring water-supply pump stations working in high efficiency area. Based on accurate poly-fitted water pump characteristic curves, an optimal scheduling models of water-supply pump stations based on its least shaft power is proposed here. Since the constraints here are mixed with inequality, nonlinear conditions, continuous variables and discrete variables, an improved adaptive genetic algorithm(IAGA) is developed and used here, which is equipped with adaptive crossover and mutation operators based on sigmoid function, as well as adaptive penalty function factor using simulated annealing. The experimental results, comparing with the results based on simple genetic algorithm, show that pump optimal operation can ensure that all the pumps would operate in high efficiency area and meanwhile, reduce electricity energy effectively, which, as a result, implies that IAGA is more suitable for optimal scheduling of water-supply pump station.

关键词： optimal scheduling constraints penalty function improved adaptive genetic algorithm simulated annealing

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Optimal scheduling of water-supply pump stations based on improved adaptive genetic algorithm

Optimal scheduling of water-supply pump stations based on im...

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第35届中国控制会议

作者： Jiayu Zhu Jingcheng Wang Xiaocheng Li Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9781509009107

关键词： optimal scheduling constraints penalty function improved adaptive genetic algorithm simulated annealing

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