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NASA/ESA Conference on adaptive Hardware and Systems (AHS)

作者： Wang Zijie Liu Rongke Mu Weiqing Shi Xiaoyan Thompson, John Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Univ Edinburgh Inst Digital Commun Edinburgh EH9 3JL Midlothian Scotland

ISBN: (纸本)9781538677537

The Radio Frequency (RF) relative measurement is a novel and compelling technology in the spacecraft formation Hying aimed at detecting, acquiring, and tracking the relative position and attitude between spacecrafts. In the development of RF relative measurement sensors, it has been found that the locations of antennas affect the measurement accuracy of the relative position and attitude to a large extend. Thus, in this study, the selection of antenna location is modeled as a function optimization problem and an adaptive genetic algorithm-based optimization method is proposed to solve it. This method can adaptively optimize the locations of antennas on the spacecraft to improve the accuracy of RF relative measurement. The experimental results demonstrate that the proposed method can indeed improve the measurement accuracy and can be adapted for different mission requirements.

关键词： RF relative measurement Spacecraft formation Hying Antenna location adaptive genetic algorithm

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Optimal Placement of PMUs Using adaptive genetic algorithm Considering Measurement Redundancy

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INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING 2016年第3期23卷 1640001-1640001页

作者： Zhou, Xin Sun, Haishun Zhang, Cong Dai, Qiangsheng Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan Peoples R China

With the extensive use of PMUs in power system, how to implement the optimal PMU placement for complete system observability is becoming more and more significant. Against simple genetic algorithm, easy to fall into local minima, this paper proposed a simple and effective adaptive genetic algorithm, aimed at determining the optimal placement of PMUs. The proposed algorithm provides adaptive crossover and mutation probability in iterations and has better convergence and global search ability than simple genetic algorithm, confirmed in many practical applications. Generally speaking, minimum PMU placement problem has multiple solutions, and this paper proposed an index of observability redundancy to rank these multiple solutions. Moreover, several observability rules were also put forward and proved with great help in obtaining the optimal solution. Simulations using MATLAB are conducted on IEEE 7 bus, 14 bus, 24 bus, 30 bus, 57 bus, 118 bus and New England 39 bus test systems in order to verify the validity of this adaptive method. Results compared with other existing methods show that the proposed adaptive method is simple to realize and obtains the optimal solution with high redundancy in very few generations, which is much more effective than the results by major existing algorithms and shows a good reference value.

关键词： Phasor measurement unit adaptive genetic algorithm observability analysis observability redundancy optimal PMU placement

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Noise free image restoration using hybrid filter with adaptive genetic algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2016年第0期54卷 382-392页

作者： Sankaran, K. Sakthidasan Nagappan, N. Velmurugan Adhiparasakthi Engn Coll Dept Elect & Commun Engn Melmaruvathur 603319 Tamil Nadu India Anna Univ APEC Madras Tamil Nadu India

Removal of noise and restoration of images has been one of the most interesting researches in the field of image processing in the past few years. Existing filter-based methods can remove image noise;however, they cannot preserve image quality and information such as lines and edges. The proposed noise reduction image restoration technique initially performs noise removal by employing a hybrid denoising filter with an adaptive genetic algorithm. The performance of the proposed technique is evaluated by comparing the result for the proposed technique with existing denoising filters, and genetic algorithm and particle swarm optimisation methods. The comparison results show that the proposed method achieves higher-quality denoising and a high restoration ratio for noisy images than the existing methods. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Image denoising Image restoration genetic algorithm Median filter Bilateral filter adaptive genetic algorithm

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Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm

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APPLIED SOFT COMPUTING 2015年 26卷 435-443页

作者： Chen, Rong Liang, Chang-Yong Hong, Wei-Chiang Gu, Dong-Xiao Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Bengbu Univ Dept Econ & Management Bengbu 233000 Anhui Peoples R China Minist Educ Engn Res Ctr Intelligent Decis Making & Informat Syst Hefei 230009 Anhui Peoples R China Oriental Inst Technol Dept Informat Management Panchiao 220 New Taipei Taiwan

Accurate holiday daily tourist flow forecasting is always the most important issue in tourism industry. However, it is found that holiday daily tourist flow demonstrates a complex nonlinear characteristic and obvious seasonal tendency from different periods of holidays as well as the seasonal nature of climates. Support vector regression (SVR) has been widely applied to deal with nonlinear time series forecasting problems, but it suffers from the critical parameters selection and the influence of seasonal tendency. This article proposes an approach which hybridizes SVR model with adaptive genetic algorithm (AGA) and the seasonal index adjustment, namely AGA-SSVR, to forecast holiday daily tourist flow. In addition, holiday daily tourist flow data from 2008 to 2012 for Mountain Huangshan in China are employed as numerical examples to validate the performance of the proposed model. The experimental results indicate that the AGA-SSVR model is an effective approach with more accuracy than the other alternative models including AGA-SVR and back-propagation neural network (BPNN). (C) 2014 Elsevier B.V. All rights reserved.

关键词： Holiday daily tourist flow forecasting Support vector regression adaptive genetic algorithm Seasonal index adjustment Back-propagation neural network

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Parameter Optimization of Friction Pendulum Bearings Based on the adaptive genetic algorithm Considering the Overall Evolutionary Status

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BUILDINGS 2024年第2期14卷 435页

作者： Yin, Guanghua Ma, Minglei Jia, Peng Ma, Xinxu China Construct 8th Engn Bur Shanghai 200120 Peoples R China

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized. In response to the situation in this paper, the optimal friction coefficient of the friction pendulum bearing is determined to be 0.01 and the optimal equivalent radius is 3.3 m. This can provide a reference for the design of seismic isolation devices.

关键词： parameter optimization adaptive genetic algorithm seismic design friction pendulum bearing

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Laminate stacking sequence optimization with strength constraints using two-level approximations and adaptive genetic algorithm

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2015年第4期51卷 903-918页

作者： An, Haichao Chen, Shenyan Huang, Hai Beihang Univ Beijing 100191 Peoples R China

A stacking sequence optimization method, which is conducted on the basis of a ground laminate and utilizes a two-level approximation as well as a genetic algorithm (GA), was developed before by the authors. Compared with general GAs, this method shows lower computational costs while reaching a high level of practical feasibility. However, the published work did not involve problems constrained with a strength requirement, which is essential for laminate structures subject to multiple loading conditions. Thus, in the present study, this approach is extended to implement strength constraints for laminate stacking sequence optimizations. First, to avoid the selection of some control parameters in the GA as well as to improve its performance, the standard genetic algorithm is modified with adaptive schemes in the fitness function and GA operators. Furthermore, by adopting the first-ply failure criterion and considering the stresses/strains for each layer in the ground laminate, the concept of temporal deletion techniques is proposed to extend this approach for handling optimization problems with strength constraints. Moreover, by combining the optimizer with the general finite element software MSC. Patran/Nastran, an optimization framework is established to conduct the optimization easily. Numerical examples are performed in repeated runs to illustrate the performance of the modified approaches in the GA as well as the feasibility and efficiency of this optimization system.

关键词： Stacking sequence optimization Strength constraints adaptive genetic algorithm Two-level approximation

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Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

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APPLIED SOFT COMPUTING 2015年 34卷 655-677页

作者： Alavidoost, M. H. Tarimoradi, Mosahar Zarandi, M. H. Fazel Amirkabir Univ Technol Dept Ind Engn Computat Intelligent Syst Lab Tehran Iran Univ Toronto Knowledge Intelligent Syst Lab Toronto ON Canada

This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Assembly line balancing adaptive genetic algorithm Evolutionary operators Fuzzy numbers Fuzzy controller One-Fifth Success Rule

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Route optimization in township logistics distribution considering customer satisfaction based on adaptive genetic algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 204卷 28-42页

作者： Cui, Huixia Qiu, Jianlong Cao, Jinde Guo, Ming Chen, Xiangyong Gorbachev, Sergey Linyi Univ Sch Automat & Elect Engn Linyi Shandong Peoples R China Qufu Normal Univ Coll Engn Jining Shandong Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Natl Res Tomsk State Univ Fac Innovat Technol Lenina St 36 Tomsk 634050 Russia

With the development of the logistics economy, problems such as the timeliness of logistics distribution and the high cost of distribution have emerged. A new adaptive genetic algorithm is proposed to solve these problems. The pc and pm values of the algorithm are related to the number of iterations and the individual fitness values. To improve the local optimization ability of the algorithm, a large neighborhood search algorithm is proposed. In addition, this study establishes a soft time window town logistics distribution model with constraints. The model considers the optimal cost as the objective function and customer satisfaction as the influencing factor. In the experiment, the proposed adaptive genetic algorithm is compared with the traditional genetic algorithm, validating the effectiveness of the proposed algorithm. (c) 2022 Published by Elsevier B.V. on behalf of International Association for Mathematics and Computers in Simulation (IMACS).

关键词： Logistics control system adaptive genetic algorithm Customer satisfaction Large neighborhood search algorithm Township logistics distribution

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Optimal design of a double-vibrator ultrasonic motor using combination method of finite element method, sensitivity analysis and adaptive genetic algorithm

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SENSORS AND ACTUATORS A-PHYSICAL 2017年 266卷 1-8页

作者： Dong, Zhaopeng Yang, Ming Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai 200240 Peoples R China

Recent studies have shown that new devices with improved performance can be successful achieved by using optimization methods In this study, an optimal design of a double-vibrator ultrasonic motor using combination method of finite element method, sensitivity analysis and adaptive genetic algorithm is presented. The finite element method is the reliable method for modal and harmonic response analysis. The sensitivity analysis is employed to determine the optimal analysis parameters having high sensitivity to optimization objectives, which can significantly improve the efficiency of the optimization. The adaptive genetic algorithm is adopted to seek the optimal values of structural parameters, which is scientific and efficient for multi-objective optimization problems. After optimization, all of the design objectives have achieved significant improvements. To verify the effectiveness of this method, a prototype motor is manufactured according to the optimization results and its performance is measured. The measured results show that this method is available and effective for the optimal design of ultrasonic motors. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Ultrasonic motor Optimal design Finite element method Sensitivity analysis adaptive genetic algorithm

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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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