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第25届中国控制与决策会议

作者： Wang Xiaogang Bai Xueliang Jiang Bo College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781467355339

Parameters of mechanism model of centrifugal compressor is wide-ranging and artificial selection is difficult to solve. Transforming parameter identification problem of the multistage compressor model into an optimization problem, adaptive genetic algorithm （AGA） is used to decide the unknown parameters in the model. Model verification results show that the parameters identification can reflect the operating characteristics of centrifugal compressors and the precision of the model is improved.

关键词： Centrifugal compressor Mechanism model Parameter identification adaptive genetic algorithm

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adaptive genetic algorithm for Reliable Training Population in Plant Breeding Genomic Selection 8

Adaptive Genetic Algorithm for Reliable Training Population ...

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International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Purbarani, Sumarsih C. Wasito, Ito Kusuma, Ilham Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781509046294

Many algorithms are developed to model Genomic Estimated Breeding Value (GEBV). Modeling GEBV evolves a huge size of genotype in both terms of the dimension (columns) and the instances (rows). Good combinations of features help in predicting which phenotype is being represented. Preparing a good training population sample is assumed to be a convenient solution to deal with such complex genotype data. In this research, an adaptive genetic algorithm (AGA) is proposed. The adaptive characteristic of AGA by adjusting probabilities in crossover and mutation is expected to converge into the global optimum without getting trapped in local optima. The proposed method using AGA to optimize the feature selection and shrinkage mechanism is looked forward to provide a reliable model to be reused in other similar datasets.

关键词： plant breeding genomic selection training population adaptive genetic algorithm

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adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency

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中国物理B（英文版） 2023年第4期32卷 124-132页

作者： Jingyuan Lu Chunfeng Cui Tao Ouyang Jin Li Chaoyu He Chao Tang Jianxin Zhong School of Physics and Optoelectronics Xiangtan UniversityXiangtan 411105China Hunan Key Laboratory for Micro-Nano Energy Materials and Device Xiangtan UniversityXiangtan 411105China

The gamma-graphyne nanoribbons(γ-GYNRs)incorporating diamond-shaped segment(DSSs)with excellent ther-moelectric properties are systematically investigated by combining nonequilibrium Green's functions with adaptive genetic *** calculations show that the adaptive genetic algorithm is efficient and accurate in the process of identifying structures with excellent thermoelectric *** multiple rounds,an average of 476 candidates(only 2.88％of all 16512 candidate structures)are calculated to obtain the structures with extremely high thermoelectric conversion *** room temperature thermoelectric figure of merit(ZT)of the optimal γ-GYNR incorporating DSSs is 1.622,which is about 5.4 times higher than that of pristine γ-GYNR(length 23.693 nm and width 2.660 nm).The significant improvement of thermoelectric performance of the optimal γ-GYNR is mainly attributed to the maximum balance of inhibition of thermal conductance(proactive effect)and reduction of thermal power factor(side effect).Moreover,through exploration of the main variables affecting the genetic algorithm,it is revealed that the efficiency of the genetic algorithm can be improved by optimizing the initial population gene pool,selecting a higher individual retention rate and a lower mutation *** re-sults presented in this paper validate the effectiveness of genetic algorithm in accelerating the exploration of γ-GYNRs with high thermoelectric conversion efficiency,and could provide a new development solution for carbon-based thermoelectric materials.

关键词： adaptive genetic algorithm thermoelectric material diamond-like quantum dots gamma-graphyne nanoribbon

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A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on adaptive genetic algorithm

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Journal of Bionic Engineering 2010年第2期7卷 142-149页

作者： Xusheng Lei,Yuhu Du School of the Instrumentation Science and Opto-Electronic Engineering,Beihang University,Beijing 100191,P.R.China [a] School of the Instrumentation Science Opto-Electronic Engineering Beihang University Beijing 100191 P. R. China

This paper proposes a new adaptive linear domain system identification method for small unmanned aerial *** the flash memory integrated into the micro guide navigation control module, system records the data sequences of flighttests as inputs (control signals for servos) and outputs (aircraft’s attitude and velocity information).After data preprocessing, thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive *** identified model is verified by a series of simulations and *** between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft *** on the proposed dynamic model, the small unmanned aerial rotorcraft can perform hovering,turning, and straight flight tasks in real flight tests.

关键词： small unmanned aerial rotorcraft dynamic space model model identification adaptive genetic algorithm

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An adaptive genetic algorithm for parameter estimation of biological oscillator models to achieve target quantitative system response

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NATURAL COMPUTING 2014年第1期13卷 119-127页

作者： Strazar, Martin Mraz, Miha Zimic, Nikolaj Moskon, Miha Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia

Mathematical modeling has become an integral part of synthesizing gene regulatory networks. One of the common problems is the determination of parameters, which are a part of the model description. In the present work, we propose a customized genetic algorithm as a method to determine the parameters such that the underlying oscillatory system exhibits the target behavior. We propose a problem specific, adaptive fitness function evaluation and a method to quantify the effect of a single parameter on the system response. The properties of the algorithm are highlighted and confirmed on two test cases of synthetic biological oscillators.

关键词： adaptive genetic algorithm Biological oscillator Gene regulatory networks Parameter estimation

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Q-factor enhanced resonator biosensor optimized by adaptive genetic algorithm for glucose detection

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2023年第3期65卷 746-752页

作者： Xie, Bingfang Wang, Cong Meng, Fanyi Li, Xiao Wu, Qun Harbin Inst Technol Sch Astronaut Harbin Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Hisense Visual Technol Co Ltd R&D Deptartment LED Display Prod Line Operat Ctr Qingdao Peoples R China

This paper presents three devices of biosensors for glucose concentrations. A resonator based on the defected ground structure (DGS) was designed and the optimizations for the single square and both squares of the DGS via adaptive genetic algorithm were applied to enhance the performance of the sensors including the Q-factor and the electric field distribution. The fabricated devices of the best optimized one exhibited an enhanced Q-factor of 442 and a sensitivity of 142.2 MHz/mgml(-1), which is more than 2.37 times than the basic structure.

关键词： adaptive genetic algorithm biosensor defective ground structure high Q-factor microwave resonator

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Co-designed of network scheduling and sliding mode control for underwater shuttle based on adaptive genetic algorithm

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ASSEMBLY AUTOMATION 2018年第5期38卷 635-644页

作者： Li, Zeyu Liu, Weidong Li, Le Liu, Zhi Zhang, Feihu Northwestern Polytech Univ Xian Shaanxi Peoples R China Second Ship Design & Res Inst Wuhan Sci & Technol Thermal Energy & Power Lab Wuhan Hubei Peoples R China

Purpose Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle's heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling. Design/methodology/approach First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation. Findings The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased. Originality/value The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

关键词： adaptive genetic algorithm Network control system

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Optimization design of the modified SST based on adaptive genetic algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2024年第4期75卷 479-495页

作者： Zhao, Hanyu Zhang, Yueyue Tu, Jianjian Zheng, Fan Li, Jiale Tianjin Univ Technol Sch Elect Engn & Automat Tianjin Peoples R China Tianjin Univ Technol Tianjin Key Lab New Energy Power Convers Transmiss & Intelligent Control Tianjin Peoples R China

The coupling of magnetic, thermal and structural force fields exists in the design, manufacture and operation of the magnetic core of transformer and motor, which makes the experimental data obtained from the standard magnetic properties measurement inconsistent with the actual problem. In order to obtain the magnetic properties measurement data of soft magnetic materials such as electrical steel sheet under multiple physical factors, the magnetic circuit structure was improved on the basis of the standard single sheet tester. At the same time, anti-bending clips made of polyether ether ketone were installed in order to prevent the bending deformation of the sample sheet during the application of tensile stress which would lead to uneven force. Then0 this basic structure of the tester under the coupling of temperature and stress is designed. In order to further improve the excitation performance of the tester and the magnetization uniformity of the sample, combined with COMSOL and MATLAB co-simulation, adaptive genetic algorithm was used to optimize the size of the yoke and excitation coil, thereby ensuring the uniformity of the magnetic field distribution in the measurement area of the sample. Finally, by combining simulation and specific experiments, the loss measurement results of a single sheet tester under different ambient temperatures and different stresses are studied, which proves that the tester is feasible and accurate, and the variation law of complex magnetic characteristics of electrical steel sheets under multiple physical fields is summarized.

关键词： Single sheet tester multiple physical factors adaptive genetic algorithm COMSOL MATLAB

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Performance-driven MCM partitioning through an adaptive genetic algorithm

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 1996年第4期4卷 434-444页

作者： Raman, S Patnaik, LM INDIAN INST SCI MICROPROCESSOR APPLICAT LABBANGALORE 560012KARNATAKAINDIA

We present a novel genetic algorithm-based partitioning scheme for multichip modules (MCM's) which integrates four performance constraints simultaneously: pin count, area, heat dissipation, and timing. We also present a similar partitioning algorithm based on evolutionary programming. Experimental studies demonstrate the superiority of these methods over deterministic Fiduccia-Mattheyes (FM) algorithm and simulated annealing (SA) technique. Our approach performs better than another genetic algorithm-based method recently reported. The adaptive change of crossover and mutation probabilities results in better convergence of the partitioning algorithm.

关键词： circuit layout CAD convergence of numerical methods genetic algorithms multichip modules probability adaptive genetic algorithm area constraint crossover probabilities evolutionary programming heat dissipation mutation probabilities partitioning algorith adaptive genetic algorithm genetic algorithm (GA) convergence of numerical methods circuit layout CAD Multichip modules HEAT DISSIPATION Partitioning algorithms evolutionary algorithm

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Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4661-4667页

作者： Lee, Chien-Pang Lin, Wen-Shin Chen, Yuh-Min Kuo, Bo-Jein Natl Chung Hsing Univ Dept Agron Div Biometry Taichung 40227 Taiwan China Med Univ Sch Nursing Taichung 40402 Taiwan

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Gene selection Sample classification adaptive genetic algorithm k-Nearest neighbor Microarray data

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