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

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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Many-objective optimization based path planning of multiple UAVs in oilfield inspection

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APPLIED INTELLIGENCE 2022年第11期52卷 12668-12683页

作者： Li, Kun Yan, Xinxin Han, Ying Ge, Fawei Jiang, Yu Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China CCTEG Shenyang Res Inst Fushun 113122 Liaoning Peoples R China

Unmanned aerial vehicles (UAVs) inspection can be considered as a path planning problem for the UAVs, which needs UAVs to traverse all task points one by one by avoiding some obstacles. In most current researches, only single objective function is used in the path planning for the UAVs, and some other objectives are seldom considered together, like: the number of UAVs, the required inspection time and so on. Therefore, in order to overcome the defect of using single objective function to plan the path of UAVs, a many-objective optimization based multiple UAVs path planning method is studied in this paper. Firstly, four objective functions are chosen which include the flight distance, the flight stability, the number of UAVs and the time offset of reaching the task point;considering the arrival time of UAVs at each task point is an interval value in the practical application, the time offset of reaching the task point is set as an interval objective function, which is handled by the matter-element extension method in this paper. Then, an improved NSGA-III algorithm is proposed to solve the established many-objective optimization problem, which uses fruit fly optimization algorithm (FOA) to replace the genetic algorithm (GA) in the NSGA-III algorithm. Finally, several evaluation indictors (Spread, generational distance (GD), inverse generational distance (IGD), and running time) are used to choose the optimal flight paths of multiple UAVs. Through simulation comparisons with other algorithms, it is concluded that the improved NSGA-III algorithm is more effective in the path planning of UAVs.

关键词： Oilfield inspection Many-objective optimization NSGA-III algorithm Path planning fruit fly optimization algorithm

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Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

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NEUROCOMPUTING 2016年 211卷 41-52页

作者： Yu, Yang Li, Yancheng Li, Jianchun Gu, Xiaoyu Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Support vector regression fruit fly optimization algorithm Self-adaptive step Magnetorheological elastomer base isolator Dynamic response prediction

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Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 1期38卷 743-771页

作者： Yu, Helong Li, Wenshu Chen, Chengcheng Liang, Jie Gui, Wenyong Wang, Mingjing Chen, Huiling Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Technol Sydney Sch Comp Sci Fac Engn & IT Ultimo Australia Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

The fruit fly optimization algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

关键词： Swarm intelligence fruit fly optimization algorithm Gaussian bare-bones Dynamic step length Engineering design problems

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Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China

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ENERGY 2018年 155卷 1013-1031页

作者： Wang, Lin Lv, Sheng-Xiang Zeng, Yu-Rong Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Wuhan 430205 Hubei Peoples R China

Accurate electricity consumption forecasting is a challenging task for its unstable behavior and influence mechanism based on multiple factors. In this study, a neural network ensemble approach is designed to solve this problem. In the proposed method, a novel sparse adaboost (adaboost(sp)) is designed as the ensemble framework to enhance the generalization ability and reduce ensemble cost, and echo state network (ESN) is adopted to build the nonlinear relationships between electricity demand and multiple factors. An improved fruit fly optimization algorithm (FOA) helps selecting input variables considering their time lag effects. Two industrial electricity consumption (IEC) forecasting applications in China are investigated to verify the effectiveness of proposed ensemble forecasting approach. Numerical results indicate that adaboost(sp)-ESN with FOA can better predict the future IEC than various benchmark methods. Compared with existing boosting ensemble approaches, the proposed adaboostsp is more efficient and can save considerable computation cost. Impacts of selected variables are further examined and results show many industrial indexes have significant time lag effects on IEC. Based on the proposed techniques, future IEC demand in Hubei Province is estimated and analyzed. Application studies demonstrate the proposed hybrid ensemble approach is a practical choice for mid-term IEC adjustment and projection. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sparse adaboost Echo state network fruit fly optimization algorithm Ensemble forecasting Industrial electricity consumption forecasting

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Free-form surface parts quality inspection optimization with a novel sampling method

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APPLIED SOFT COMPUTING 2018年 62卷 550-570页

作者： Li, Taifeng Gao, Liang Pan, Quanke Li, Peigen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Quality inspection is a critical step in the process of manufacturing of free-form surface parts. For dimensional quality control, registration is normally used to match the measurement model to the design model. Then, the machined errors can be visualized in the misalignment regions. Because these two models are located in different coordinate systems, an optimal spatial transformation matrix is necessary. In this area, iterative closest point (ICP) is the best known method for registration. However, the global optimum solution is intractable to compute in terms of ICP. Considering of the accuracy and efficiency of free-form surface parts quality inspection, in this paper, the maximin distance approach is introduced for model simplification with normal deviation information. Moreover, to find the optimal transformation parameters, an improved fruit fly optimization algorithm is proposed in terms of the saccade behavior of drosophila. The performance of the developed algorithms has been tested via both computer simulations and real experiments, and the results demonstrate that the proposed methods are superior to several classical approaches for free-form surface parts quality inspection. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Quality inspection Registration Maximin distance method fruit fly optimization algorithm

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Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

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MARINE STRUCTURES 2020年 72卷 102762-102762页

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

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.

关键词： Offshore oil and gas industry FPSO Strain prediction BP neural Network fruit fly optimization algorithm

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An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第7期42卷 1281-1293页

作者： Han, Ying Jing, Yuanwei Dimirovski, Georgi M. Northeastern Univ 11Lane 3Wenhua Rd Shenyang 110819 Liaoning Peoples R China Dogus Univ Istanbul Turkey SS Cyril & Methodius Univ Skopje North Macedonia

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN;and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method.

关键词： Time series prediction network traffic echo state network unscented Kalman filter fruit fly optimization algorithm

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