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Prediction of microfibril angle in Dahurian larch wood using visible-near infrared spectroscopy and chemometric techniques

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2019年第4期27卷 270-277页

作者： Li, Ying Via, Brian K. Cheng, Qingzheng Li, Yaoxiang Northeast Forestry Univ Coll Engn & Technol Harbin 150040 Heilongjiang Peoples R China Auburn Univ Forest Prod Dev Ctr SFWS Auburn AL 36849 USA

The microfibril angle of the S2 layer in the secondary cell wall of the tracheid is important for molecular and microscopic properties that influence collapse resistance, longitudinal modulus of elasticity and other lateral properties of conifers at the macroscopic level. This research aimed to investigate the feasibility of using a fruit fly optimization algorithm for visible and near infrared modeling optimization of Dahurian larch wood microfibril angle prediction. Originally, the linear relationship between microfibril angle and their raw spectra and visible and near infrared spectra pretreated by wavelet transform was established. Then, a nonlinear coupled model was built by combining the stepwise regression analysis and generalized regression neural network methods. As a final point, fruit fly optimization algorithm was used for optimizing stepwise regression analysis- generalized regression neural network coupled model. It was found that stepwise regression analysis-generalized regression neural network coupled model coupled model based on the optimization of fruit fly optimization algorithm simplify visible and near infrared spectral data and its prediction results ( r(p)(2) = 0.90, RMSEP = 0.75, mean average percentage error ( MAPE(p)) = 0.05) outperforms original partial least squares model ( r(p)(2) = 0.86, RMSEP = 0.88, MAPE(p) = 0.06). This work demonstrated the feasibility of using improved chemometric techniques for improving the precision of visible and near infrared spectra in the prediction of microfibril angle.

关键词： Visible and near infrared spectroscopy stepwise regression analysis-generalized regression neural network coupled model fruit fly optimization algorithm wavelet transform microfibril angle

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BIFFOA: A Novel Binary Improved fruit fly algorithm for Feature Selection

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IEEE ACCESS 2019年 7卷 81177-81194页

作者： Hou, Yun Li, Jingyao Yu, Haihong Li, Zhanshan Natl Educ Minist Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Coll Software Changchun 130012 Jilin Peoples R China

Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification. It eliminates irrelevant, redundant, and noisy features improves the performance of the model and reduces the computational burden. fruit fly optimization algorithm is a newalgorithm proposed in recent years, which imitates the foraging behavior of fruit fly. To the best of our knowledge, it has not been systematically applied to feature selection. This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named binary improved fruit fly optimization algorithm (BIFFOA). Besides, four different strategies based on evolutionary population dynamics (EPD) and new mutation operators are employed to enhance the BIFFOA. The extensive experiments on 25 datasets (see Table 1) show that the performance of the BIFFOA is better than several state-of-the-art algorithms.

关键词： Classification evolutionary population dynamics feature selection fruit fly optimization algorithm

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Research on Path Planning of AGV Based on Improved Ant Colony optimization algorithm

Research on Path Planning of AGV Based on Improved Ant Colon...

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第33届中国控制与决策会议

作者： Jiuxiang Sun Ya'nan Yu Ling Xin College of Automation and Electronic Engineering Qingdao University of Science and Technology

Path planning is a key problem in the motion control of mobile *** order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency,this paper puts forward a matrix yard storage mode,which is transformed into grid map model,and then uses ant colony optimization algorithm to plan the path of *** at the shortcomings of traditional ant colony optimization algorithm(ACO) in global path planning,such as slow convergence speed and weak optimization ability,an improved ant colony path planning algorithm is ***,the grid map is established,and the fruit fly optimization algorithm(FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm,and then the ant colony optimization algorithm is used for global path *** the same time,in order to solve the problem of many path turning angles and large cumulative turning angles in the planning,the path smoothing is carried *** simulation results show that the improved algorithm has fewer turns and smoother path,and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

关键词： Ant colony optimization algorithm fruit fly optimization algorithm matrix yard storage mode Smoothing

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A novel water quality mechanism modeling and eutrophication risk assessment method of lakes and reservoirs

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NONLINEAR DYNAMICS 2019年第2期96卷 1037-1053页

作者： Wang, Xiaoyi Zhou, Yuqin Zhao, Zhiyao Wang, Li Xu, Jiping Yu, Jiabin Beijing Technol & Business Univ Sch Comp & Informat Engn 11-33 Fucheng Rd Beijing 100048 Peoples R China

Water quality mechanism modeling and eutrophication analysis are important technical means for water pollution prevention and control of lake and reservoirs. However, the existing classical water quality mechanism models usually contain unknown parameters with empirical values range, which brings difficulty of predicting water quality changes of specific lakes and reservoirs to meet the accuracy requirements. Furthermore, the most existing water quality prediction methods output single-valued predictions of water quality indicators. These prediction results possess contingencies and uncertainties. Eutrophication analysis based on these results will bring errors, even become completely wrong. Therefore, combining the evolution mechanism of water quality, this paper proposes a fruit fly optimization algorithm (FFOA)-based water quality mechanism modeling method and studies a eutrophication risk assessment method of lakes and reservoirs based on the mechanism model. First, combining observed water quality data, unknown parameters of a water quality mechanism model are estimated by using FFOA. Then, Monte Carlo simulation is employed to predict the evolution of water quality, and the probability distribution functions (PDFs) of water quality indicators at predicted time indexes are obtained to achieve water quality prediction. Finally, to quantitatively assess the eutrophication status, a comprehensive eutrophication status index (CESI) is constructed. The PDF of CESI and the probabilities of being in different eutrophication levels are calculated to achieve water eutrophication risk assessment. The simulation results show that the proposed method can effectively estimate the unknown parameters of the water quality mechanism model, predict water quality evolution and assess the eutrophication risk with better accuracy and rationality by comparing the existing methods.

关键词： Water quality mechanism model fruit fly optimization algorithm Monte Carlo simulation Water quality prediction Eutrophication Risk assessment

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Parameter optimization for FPSO design using an improved FOA and IFOA-BP neural network

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OCEAN ENGINEERING 2019年第Mar.1期175卷 50-61页

作者： Wu, Lei Yang, Yaowen Maheshwari, Muneesh Li, Ning Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Nanyang Technol Univ Maritime Inst Singapore 639798 Singapore China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China

In the offshore oil industry, FPSO (floating, production, storage and offloading) units play a leading role for the production, processing and storage of oil. The hull girder strength of FPSO, which is related to the safety and economic aspects, is usually designed based on engineers' experience. In this study, a novel method is presented to optimize the FPSO design parameters which mainly affect the hull girder strength. The proposed method employs an improved fruit fly optimization algorithm (IFOA) and IFOA-BP model which combines IFOA and back-propagation (BP) neural network. Firstly, the IFOA-BP model maps the nonlinear relations between the input and output variables, and then the reserved network can predict the stress value of critical position and the self-weight of FPSO for any set of design parameters. The numerical results indicate that the IFOA-BP model has a remarkable predication ability. Further, the reserved IFOA-BP model and the proposed IFOA is used to search for the optimal set of design parameters. Compared with the contrastive design, the optimal set of design parameters obtained using the proposed method gives lower stress value of critical position and smaller self weight of FPSO. The optimization results show the advance and superiority of the proposed method.

关键词： FPSO Parameter optimization fruit fly optimization algorithm Back-propagation neural network Stress

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An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain

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IEEE ACCESS 2019年 7卷 80849-80860页

作者： Liu, Junxiu Huang, Jiadong Luo, Yuling Cao, Lvchen Yang, Su Wei, Duqu Zhou, Ronglong Guangxi Normal Univ Sch Elect Engn Guilin 541004 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Ulster Univ Sch Comp Engn & Intelligent Syst Londonderry BT48 7JL North Ireland

In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding process, the host image is decomposed into a number of sub-bands through multilevel DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.

关键词： Image watermarking discrete wave transformation singular value decomposition Hessenberg decomposition fruit fly optimization algorithm

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A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments

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APPLIED INTELLIGENCE 2019年第7期49卷 2603-2622页

作者： Tang, Hongwei Sun, Wei Yu, Hongshan Lin, Anping Xue, Min Song, Yuxue Hunan Univ State Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Shaoyang Univ Hunan Prov Key Lab Grids Operat & Control Multipo Shaoyang 422000 Peoples R China Hunan Univ Hunan Prov Key Lab Intelligent Robot Technol Elec Changsha 410082 Hunan Peoples R China

In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm optimization (PSO) and fruit fly optimization algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this paper presents a novel multi-swarm hybrid FOA-PSO (MFPSO) algorithm for robot target searching. The main contributions of the proposed method are as follows. (1) The improved FOA (IFOA) provides a better value for the improved PSO (IPSO) to find the next optimal robot position value. (2) Multi-swarm strategy is introduced to enhance the diversity and achieve an effective exploration to avoid premature convergence and falling into local optima. (3) An escape mechanism named MSCM (Multi-Scale Cooperative Mutation) is used to address the limitation of local optima and enhance the escape ability for obstacle avoidance. All of the aspects mentioned above lead robots to the target without falling into local optima and allow the search mission to be performed more quickly. Several experiments in four parts are performed to verify the better performance of MFPSO. The experimental results show that the performance of MFPSO is much more significant than that of other current approaches.

关键词： Particle swarm optimization fruit fly optimization algorithm Target searching Multi-swarm Multi-scale cooperative mutation

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A dynamic SVR-ARMA model with improved fruit fly algorithm for the nonlinear fiber stretching process

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NATURAL COMPUTING 2019年第4期18卷 747-756页

作者： Guo, Fan Ren, Lihong Jin, Yaochu Ding, Yongsheng Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Univ Surrey Dept Comp Sci Guildford GU27XH Surrey England

The fiber stretching process plays the key role in the process of fiber production and its effects is measured by the stretching ratio. The stretching ratio is determined by the relative speed of the winding roller. The stretching ratio has impact on the performance of the final fiber filament and production directly. Focused on the importance of the stretching ratio, the support vector regression (SVR) predictive model, called nonlinear auto-regressive exogenous model, for the fiber stretching rate based on existing industry data is proposed. Furthermore, the fruit fly optimization algorithm inspired by immune mechanism and cooperation functional (IFOA) is presented, and then is used to optimize the parameters in SVR. Furthermore, taking into account the high cost and accurate precision of the fiber stretching process, a time series autoregressive moving average (ARMA) model is introduced to reduce the prediction error of the IFOA-SVR model. Simulations results demonstrate that the proposed IFOA-SVR method can increase the prediction accuracy than the traditional FOA and the SVR method, and the ARMA model is essential to modify the prediction error of the IFOA-SVR model.

关键词： SVR model ARMA model fruit fly optimization algorithm Fiber stretching process Prediction

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Path planning of multiple UAVs with online changing tasks by an ORPFOA algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 94卷 103807-103807页

作者： Li, Kun Ge, Fawei Han, Ying Wang, Yi'an Xu, Wensu Bohai Univ Coll Engn Jinzhou 121013 Liaoning Peoples R China

The unmanned aerial vehicle (UAV) is a new type oilfield inspection tool which is characterized by high flexibility, low cost and high efficiency. In the UAV based oilfield inspection technology, the path planning is an indispensable element which finds an optimal flight path for UAV to finish the inspection jobs successfully. In comparison with the other researches, our study focuses on two challenging issues: path planning of multiple UAVs by traversing a certain amount of task points in the three-dimensional environment within the required completion time, and optimizing solving for the best flight path with online changing tasks. In the research, a novel task assignment method including the initial task assignment and the task assignment with changing tasks is proposed to determine the initial task sequences of each UAV and rapidly replan task sequences after tasks change. An improved fruit fly optimization algorithm (named ORPFOA) is proposed to solve the path planning problem in both initial task sequences and new task sequences after tasks change, in which the optimal reference point and a distance cost matrix are used to reach both faster solving and higher optimizing precision for the optimal flight path. In ORPFOA, two cost functions are defined to evaluate the optimizing results in the initial phase and the new phase after task changes, respectively. A simulation model of the three-dimensional oilfield environment is established to verify the effectiveness of the proposed method in comparison with other six algorithms.

关键词： Oilfield inspection UAVs Path planning Task assignment Swarm intelligence algorithm fruit fly optimization algorithm

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Optimal attitude tracking control for an unmanned aerial quadrotor under lumped disturbances

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INTERNATIONAL JOURNAL OF MICRO AIR VEHICLES 2020年第12期12卷 -页

作者： Ding, Li Li, Yangmin Jiangsu Univ Technol Coll Mech Engn Changzhou 213000 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

The robust control problem in attitude tracking of an unmanned aerial vehicle quadrotor is a challenging task due to strong parametric uncertainties, large nonlinearities and high couplings in flight dynamics. In this paper, a continuous nonsingular fast terminal sliding mode controller based on linear extended state observer is proposed for attitude tracking control of a quadrotor under lumped disturbances. The proposed control method requires no prior knowledge of the attitude dynamics. It can ensure rapid convergence rate and high tracking precision due to terminal sliding mode surface and fast reaching law. The controller uses the linear extended state observer to reject the influence of both parametric uncertainties and external disturbances. Meanwhile, the nonsingular fast terminal sliding mode control strategy is designed to ensure the state variables to slide to desired points in finite time. To enhance the control performance, a self-adaptive fruit fly optimization algorithm is applied to parameters tuning of the proposed controller. The effectiveness of the proposed control approach is illustrated through numerical simulations and experimental verification.

关键词： Quadrotor attitude tracking control terminal sliding mode linear extended state observer fruit fly optimization algorithm

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