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Chinese Automation Congress (CAC)

作者： Liu Kaiwei Wang Xingcheng Wang Longda Liu Gang Dalian Maritime Univ Ship Elect Engn Coll Dalian Peoples R China Inner Mongolia Univ Nationalities Coll Mech Engn Tongliao Peoples R China

ISBN: (纸本)9781728113128

In this paper. the complex multi-objective optimization problem is investigated for the train operation process. To sovle the multi-objective optimization problem, the fruit fly optimization algorithm (FOA) is adopted. Fistly, a new method of distance fusion, which fuses the Mahalanobis distance and the Euclidean distance, is proposed to solve the problem that the classical Mahalanobis distance and Euclidean distance can't. effectively calculate the actual distance between the individual and the extremum individual solution set caused by the correlation ambiguity of characteristic variables. Then, to maintain the population diversity, the preference information is adopted so that the optimal solution provided by FOA can significantly move to the desired region. Hence, both problems can be overcome by the improved FOA based on fusion distance and preference information. Finally, simulation results are carried out to show the efficacy of the proposed method.

关键词： train operation optimization multi-objective optimization fruit fly optimization algorithm distance measurement preference information

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Network Traffic Prediction Using Variational Mode Decomposition and Multi-Reservoirs Echo State Network

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IEEE ACCESS 2019年 7卷 138364-138377页

作者： Han, Ying Jing, Yuanwei Li, Kun Dimirovski, Georgi Marko Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Bohai Univ Coll Engn Jinzhou 121013 Peoples R China Dogus Univ Sch Engn Dept TR-34722 Istanbul Turkey St Cyril & Methodius Univ Skopje Sch FEIT Skopje 1000 North Macedonia

The network traffic prediction is significant for the network load pre-warning and network congestion control. But the nonlinearity and nonstationarity of the actual network traffic data would reduce the prediction accuracy. In this paper, an optimized network traffic prediction method using variational mode decomposition (VMD) and multi-reservoirs echo state network (ESN) is presented. VMD method has advantages of reducing the signal transmission errors, removing the mode aliasing, and decreasing the degree of endpoint effects. However, VMD needs to preset the number of modes and the iterative factor, which are mainly decided by subjective experiences. In order to solve this, an optimized VMD method is proposed, and then a multi-reservoirs echo state network based prediction model is constructed. The main works are as follows: First, VMD is used to decompose the original network traffic data into several subsets;then, multiple subreservoirs are built after the phase space reconstruction (PSR) of each data subset;finally, the training set is used to train the prediction model. Moreover, in the training process, an improved fruit fly optimization algorithm (IFOA) is proposed combined with the levy's flight function and the cloud generator, which is used to optimize some model parameters. Compared with several prediction models, the proposed VMD-IFOA-ESN has better predictive stability and convergence performance. Three WIDE backbone network traffic data sets with different time intervals verify the effectiveness of the proposed prediction model.

关键词： Echo state network fruit fly optimization algorithm multiple reservoirs network traffic prediction time series analysis variational modal decomposition

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Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

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ENERGIES 2017年第12期10卷 2001页

作者： Niu, Dongxiao Liang, Yi Hong, Wei-Chiang North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Oriental Inst Technol Dept Informat Management New Taipei 220 Taiwan

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting.

关键词： wind speed forecasting empirical mode decomposition general regression neural network fruit fly optimization algorithm

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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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Two-Stage optimization Model of Agricultural Product Distribution in Remote Rural Areas

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IEEE ACCESS 2020年 8卷 213928-213949页

作者： Zhang, Hao Feng, Huixia Wang, Hongmei Beijing Technol & Business Univ Sch E Business & Logist Beijing 100048 Peoples R China Beijing Jiaotong Univ Beijing 100044 Peoples R China Beijing Food Safety Res Base Beijing 100044 Peoples R China

Due to the lack of systematically optimized logistics networks in many remote rural areas, the online sales of agricultural products in these areas have the disadvantages of high cost, high damage and slow speed. To address these logistic problems, this paper proposes a two-stage layout optimization model of agricultural product joint distribution centres based on the geographical features of remote rural areas. The number and location of distribution centres are selected in two stages to optimize the logistics network. Chengkou County, located in Chongqing city, China, has been selected as the study area. In the first stage, AP clustering is carried out using the distance between the logistics nodes of villages and towns, and the transit station of the source of agricultural products is obtained using the correlation between the nodes, which are regarded as alternative locations for the second-level logistics nodes. In the second stage, a joint distribution centre optimization model with the lowest cost is constructed. A fruit fly optimization algorithm is used to select the optimal locations from the alternative locations as the second-level logistics nodes and obtain the specific delivery path. Optimizing the logistics network of remote rural areas can reduce the logistics costs of agricultural products in these areas, promote online sales of agricultural products, and provide the government and logistics companies with theoretical references to open up new markets in remote areas.

关键词： Logistics Agricultural products optimization Mathematical model Heuristic algorithms Transportation Routing AP clustering fruit fly optimization algorithm joint distribution centres two-stage logistics layout optimization

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A new optimization particle filtering navigation location method for aquatic plants cleaning workboat in crab farming

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第4期15卷

作者： Ruan, Chengzhi Zhao, Dean Ding, Shihong Sun, Yueping Rao, Jinhui Liu, Xiaoyang Jia, Weikuan Wuyi Univ Sch Mech & Elect Engn 358 Baihua Rd Wuyishan 354300 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the mean(R) of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network-particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the mean(T) of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.

关键词： Crab farming aquatic plants cleaning workboat visual navigation particle filter generalized regression neural network fruit fly optimization algorithm

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Improved Neural Network Control Approach for a Humanoid Arm

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2019年第10期141卷 1-13页

作者： Liu, Xinhua Zhang, Xiaohui Malekian, Reza Sarkodie-Gyan, Th. Li, Zhixiong China Univ Min & Technol Sch Mechatron Engn Xuzhou 211006 Jiangsu Peoples R China Malmo Univ Dept Comp Sci & Media Technol S-20506 Malmo Sweden Univ Texas El Paso Coll Engn 500 West Univ Ave El Paso TX 79968 USA Ocean Univ China Sch Engn Tsingdao 266100 Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia

This study extended the knowledge over the improvement of the control performance for a seven degrees-of-freedom (7DOF) humanoid arm. An improved adaptive Gaussian radius basic function neural network (RBFNN) approach was proposed to ensure the reliability and stability of the humanoid arm control. Considering model uncertainties, the established dynamic model for the humanoid arm was divided into a nominal model and an error model. The error model was approximated by the RBFNN learning to compensate the uncertainties. The contribution of this study mainly concentrates on employing fruit fly optimization algorithm (FOA) to optimize the basic width parameter of the RBFNN, which can enhance the capability of the error approximation speed. Additionally, the output weights of the neural network were adjusted using the Lyapunov stability theory to improve the robustness of the RBFN-based error model. The simulation and experiment results demonstrate that the proposed approach is able to optimize the system state with less tracking errors, regulate the uncertain nonlinear dynamic characteristics, and effectively reduce unexpected interferences.

关键词： adaptive control fruit fly optimization algorithm humanoid arm radial basis function network

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Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2022年 17卷

作者： Jiang, Hao Song, Jiuxiang Zhang, Baowei Wang, Yonghua Zhengzhou Univ Light Ind 5 Dongfeng Rd Zhengzhou 450002 Henan Peoples R China

Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiveness. The experimental results show that the generalized regression neural network optimized by gray wolf optimizer has the best effect and the fastest optimization speed;the generalized regression neural network optimized by particle swarm optimization algorithm has the middle optimization speed;the generalized regression neural network optimized by fruit fly optimization algorithm has the worst effect and the slowest optimization speed.

关键词： Yarn unevenness prediction few sample dataset generalized regression neural network particle swarm optimization fruit fly optimization algorithm gray wolf optimizer

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Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 142卷 112999-000页

作者： Chen, Huiling Li, Shimin Heidari, Ali Asghar Wang, Pengjun Li, Jiawei Yang, Yutao Wang, Mingjing Huang, Changcheng Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

The original fruit fly algorithm (FOA) in simple structure is easy to understand, but it has a slow convergence rate and tends to be trapped in the local optimal solutions. In order to improve the convergence rate and efficacy of FOA, two new mechanisms are integrated with the exploratory and exploitative strategies of the original FOA: the outpost mechanism and the multi-population mechanism. The outpost mechanism consists of two parts: greedy selection and Gaussian mutation, which is mainly used to improve the convergence rate of the algorithm. The multi-swarm mechanism divides the population of agents into several sub-swarms and selects several individuals from sub-swarm with a random probability. Then, the selected individuals are remapped into the feature space to expand the exploratory capabilities. To illustrate the performance of the proposed method, a comprehensive set of benchmark functions, including the unimodal, multimodal, and composition functions were chosen for testing tasks. Also, the proposed MOFOA is compared against the state-of-the-art improved FOA algorithms and other well-known swarm-based methods. The experimental results have shown that MOFOA can outperform all the competitors involved in this study in terms of convergence speed and solution quality in a significant manner. Furthermore, MOFOA is also employed to optimize two critical parameters of the support vector machine (SVM) for classification tasks. The results demonstrate that the proposed MOFOA can also achieve a better performance than other swarm-based methods in dealing with the optimization of the SVM in dealing with several financial datasets. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Parameter optimization Outpost mechanism Multi-population mechanism Support vector machine

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