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Parameter optimization of plug-in hybrid electric vehicle based on quantum genetic algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14835-14843页

作者： Zeng, Yuping Nanchang Inst Technol Jiangxi Prov Key Lab Precis Drive & Control Nanchang Jiangxi Peoples R China

The parameters of the power system and the control strategy parameters are coupled,they work together to affect the power, economy and emission of the plug-in hybrid electric vehicle. Firstly, the real-time optimal control strategy based on the optimum of the vehicle power system's efficiency is developed. Secondly, the cost of the power system and indicators of vehicle's power performance are taken as constraints, the fuel consumption and emissions considering the engine's cold-heat effect are taken as the optimization objective, and in order to obtain the optimum solution of the power system's parameters and control strategy parameters, the standard genetic algorithm (SGA) and the quantum genetic algorithm (QGA) are applied to coordinated optimize them respectively. Finally, the simulation model of the vehicle system is established. After simulation, the simulation results show that compared with the original vehicle, the fuel economy and emission performance of the vehicle which parameters optimized by SGA and QGA has greatly improved, compared to the parameters optimization by SGA, the optimization by QGA can suppress premature convergence and have better fuel economy and emission performance.

关键词： Plug-in hybrid vehicle Coordinated optimization quantum genetic algorithm Fuel consumption Emission

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Optimization of Deterministic Pilot Pattern Placement Based on quantum genetic algorithm for Sparse Channel Estimation in OFDM Systems

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IEICE TRANSACTIONS ON COMMUNICATIONS 2020年第10期E103B卷 1164-1171页

作者： Nie, Yang Yu, Xinle Commun Univ China Beijing 100024 Peoples R China

This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.

关键词： pilot pattern quantum genetic algorithm sparse channel estimation mutual incoherence property compressed sensing

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Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by quantum genetic algorithm

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IEEE ACCESS 2018年 6卷 33583-33588页

作者： Zhu, Xingtong Xiong, Jianbin Liang, Qiong Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Guangdong Polytech Normal Univ Sch Comp Guangzhou 510665 Guangdong Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault Maoming 525000 Peoples R China

Considering the disadvantages of conventional fault diagnosis methods for rotating machinery, such as low efficiency and low accuracy, we propose a fault diagnosis method based on support vector machine (SVM) optimized by quantum genetic algorithm (QGA). First, the SVM parameters are optimized by QGA. Then, the training data set is used to train the SVM model, and the test data set is used for model testing. The experimental results show that the proposed method has higher accuracy in fault diagnosis than traditional SVM method and the method based on genetic algorithms and SVM.

关键词： Fault diagnosis quantum genetic algorithm support vector machine

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Adaptive Morphological Contrast Enhancement Based on quantum genetic algorithm for Point Target Detection

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MOBILE NETWORKS & APPLICATIONS 2021年第2期26卷 638-648页

作者： Zhang, Guofeng Hamdulla, Askar Xinjiang Univ Inst Informat Sci & Engn Urumqi 830046 Peoples R China Changji Vocat & Tech Coll Changji 831100 Peoples R China

Robust point target detection of infrared clutter background has drawn great interest of scholars. Recently, morphological filter is playing a significant role in detecting infrared point target. Generally, the background clutter and targets are diverse in the case of each image. Traditional fixed structural elements and dimensions cannot be adjusted adaptively to acquire to successful point target detection in different complex backgrounds. Therefore, a new method is introduced based on quantum genetic algorithm to optimize and obtain structural element which is used as morphological filter for small target detection in original infrared ***,morphological contrast enhancement is further proposed to enhance energy of point targets after the filtered image is ***, an enormous background clutter and noise are suppressed and the contrast between target and background are observably increased. Finally, by setting proper threshold, the point targets can be detected perfectly. Experimental evaluation results show that the proposed adaptive morphological contrast enhancement based on quantum genetic algorithm is effective and robust with respect to detection accuracy compared with the traditional morphological filter and other filtering algorithms.

关键词： Point targets Structural elements quantum genetic algorithm Morphological contrast enhancement

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A multi-granularity evolution based quantum genetic algorithm for QoS multicast routing problem in WDM networks

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COMPUTER COMMUNICATIONS 2009年第2期32卷 386-393页

作者： Xing, Huanlai Liu, Xin Jin, Xing Bai, Lin Ji, Yuefeng Beijing Univ Posts & Telecommun Minist Educ Key Lab Opt Commun & Ligthwave Technol Beijing 100876 Peoples R China

QoS multicast routing problem in WDM networks is investigated, and an improved algorithm Multi-granularity Evolution based quantum genetic algorithm (MEQGA) is proposed to address it. Based on quantum genetic algorithm (QGA) with quantum rotation gate strategy, MEQGA introduces multi-granularity evolution mechanism, which allows different chromosomes of one generation to have different rotation angle step values to update. In term of this mechanism, MEQGA can significantly improve its capability of exploration and exploitation, since its optimization performance does not over-depend on the single rotation angle step scheme shared by all chromosomes any longer. MEQGA also presents an adaptive quantum mutation operation which is able to avoid local search efficiently. A repair method is applied to eliminate illegal graphs as many as possible hence more excellent solutions will appear in each evolutionary generation. Simulation results show that, for the QoS multicast routing problem, MEQGA outperforms other heuristic algorithms and is characterized by robustness, high success ratio, fast convergence and excellent capability on global searching. (C) 2008 Elsevier B.V. All rights reserved.

关键词： WDM Multicast Quality of service quantum genetic algorithm

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Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks

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AD HOC NETWORKS 2019年 87卷 26-36页

作者： Mousavi, Sajad Afghah, Fatemeh Ashdown, Jonathan D. Turck, Kurt No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA Air Force Res Lab Rome NY USA

Task allocation among a network of heterogeneous resource-constrained Unmanned Aerial Vehicles (UAVs) in an unknown and remote environment is still a challenging problem noting the limited available information about highly dynamic environment, lack of continuous and reliable communication network, and the limited energy and resources available at the UAVs. One solution for this such allocation problem is to form several efficient coalitions of the UAVs, where a complex task is assigned to a group of agents (i.e., a coalition) carrying the required resources/capabilities to perform this task. In this paper, inspired by quantum Evolutionary algorithms, we propose a leader-follower coalition formation algorithm in a large-scale UAV network to form the best possible coalitions of agents to accomplish the detected tasks in an unknown environment. Three main objectives have been considered in this coalition formation: (i) minimizing resource consumption in completing the assigned tasks on time;(ii) enhancing the reliability of the coalitions;and (iii) considering the most trustworthy UAVs amid the self-interested UAVs in forming the coalitions. The simulation results demonstrate the superior performances of the proposed model in different scenarios with large number of UAVs compared to existing coalition formation algorithms such as merge-and-split and a famous multi-objective genetic algorithm called NSGA-II.(12) (C) 2018 Elsevier B.V. All rights reserved.

关键词： Unmanned aerial vehicles Coalition formation Task allocation Evolutionary algorithms quantum genetic algorithm

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Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and quantum genetic algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第2期31卷 486-494页

作者： Zhang, Zhiyan Guo, Xianghui Yang, Pengju Wang, Taoyun Ji, Yuqi Yao, Lina Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China State Grid Shanghai Elect Power Co Jinshan Power Supply Co Shanghai 200540 Peoples R China

With the increasing integration of distributed photovoltaic (PV) generation into distribution networks, challenges such as power reverse flow and high line losses have emerged, leading to greater uncertainty in power systems. To address these issues, this paper presents an analytical model for calculating line losses in low-voltage distribution networks with PV generation, utilizing power flow calculations. A simulation model of a 15 node low-voltage network is developed using SIMULINK to validate the accuracy of the analytical model under the scenario of uniform load distribution (ULD). Additionally, a line loss optimization algorithm based on quantum genetic algorithms (QGA) is proposed for low-voltage distribution networks with distributed PV generation, along with an optimization model. The objective function of the optimization model is based on the reduction in line losses resulting from the integration of the PV system. The example results demonstrate the consistency between the line loss optimization using QGA and the analytical results, highlighting the significant advantages of QGA in terms of speed and accuracy. This research provides valuable insights for line loss optimization in low-voltage distribution networks with distributed PV generation and serves as a theoretical reference for future studies in this field.

关键词： analytical model distribution system line loss photovoltaic generation quantum genetic algorithm

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Double chains quantum genetic algorithm with application to neuro-fuzzy controller design

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ADVANCES IN ENGINEERING SOFTWARE 2011年第10期42卷 875-886页

作者： Li, P. C. Song, K. P. Shang, F. H. NE Petr Univ Postdoctoral Res Ctr Oil & Gas Engn Daqing 163318 Heilongjiang Peoples R China

This paper proposes a double chains quantum genetic algorithm (DCQGA), and shows its application in designing neuro-fuzzy controller. In this algorithm, the chromosomes are composed of qubits whose probability amplitudes comprise gene chains. The quantum chromosomes are evolved by quantum rotation gates, and mutated by quantum non-gates. For the direction of rotation angle of quantum rotation gates, a simple determining method is proposed. The magnitude of rotation angle is computed by integrating the gradient of the fitness function. Furthermore, a normalized neuro-fuzzy controller (NNFC) is constructed and designed automatically by the proposed algorithm. Application of the DCQGA-designed NNFC to real-time control of an inverted pendulum system is discussed. Experimental results demonstrate that the designed NNFC has very satisfactory performance. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： quantum computing Evolutionary computing quantum genetic algorithm Neuro-fuzzy controller Inverted pendulum control Parameter optimization

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Adaptive Stochastic Resonance Method Based on quantum genetic algorithm and its Application in Dynamic Characteristic Identification of Bridge GNSS Monitoring Data

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IEEE ACCESS 2020年 8卷 113994-114009页

作者： Wang, Xinpeng Huang, Shengxiang Li, Guanqing Zhang, Wen Li, Chenfeng Wang, Yarong Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Beijing Key Lab Urban Spatial Informat Engn Beijing 100038 Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Peoples R China

Bridge dynamic monitoring based on GNSS has become an important means of monitoring bridge structure. GNSS dynamic monitoring signals are often overwhelmed by strong noises and multipath errors. Thus the conventional data processing methods such as Fourier transform, wavelet analysis, and others have poor denoising effect or obtain unconspicuous dynamic characteristics from weak vibration signals. To solve this problem, the present study proposes a new adaptive stochastic resonance method based on quantum genetic algorithm with known frequency as optimal parameter. Analyzing the simulation signals not only verifies the validity and scientificity of the method, but also analyzes its frequency extraction effect in the approximate error range of target frequency with different noise intensity. A notable bridge vibration frequency is obtained when the new method is applied to analyze the bridge dynamic monitoring data based on GNSS.

关键词： Bridges Stochastic resonance Monitoring Global navigation satellite system genetic algorithms Vibrations Strain Bridge dynamic characteristic identification weak signals adaptive stochastic resonance quantum genetic algorithm GNSS monitoring data

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Acoustic partial discharge localization methodology in power transformers employing the quantum genetic algorithm

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APPLIED ACOUSTICS 2016年 102卷 71-78页

作者： Liu, Hua-Long Chongqing Elect Power Co Nanan Power Supply Branch Co State Grid China Chongqing 401336 Peoples R China

Partial discharge (PD) is one of staple reasons resulting in the deterioration of insulation system in power transformers and power faults. If the PD activity can be located at an early stage, preventive maintenance measures can be taken effectively. Therefore, the localization techniques of the PD do play an extraordinarily momentous role to enhance the operating reliability and stability of the system. It is proposed that a methodology based on the quantum genetic algorithm (QGA) adopting acoustic emission (AE) techniques for locating PD in power transformers. Results of the calculating example confirm the localization effectiveness and accurateness of the proposed method are ideal and satisfactory, and that precision of localization is enhanced compared to some other localization algorithms. The acoustic localization methodology based on the QGA is feasible and of suitability for the field applications. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Partial discharge Power transformer Ultrasonic localization quantum genetic algorithm

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