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

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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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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Path Planning of UAV for Oilfield Inspection Based on Improved Grey Wolf optimization algorithm 31

Path Planning of UAV for Oilfield Inspection Based on Improv...

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31st Chinese Control And Decision Conference (CCDC)

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

ISBN: (纸本)9781728101057

It is difficult for the traditional manual inspection method to satisfy the current management requirements. Now, UAV inspection technology has been used by more and more enterprises. In the UAV inspection, path planning is an important work. An improved grey wolf optimization algorithm is proposed for the path planning of UAV in oilfield environment in this paper. Firstly, the model of the oilfield environment is built;secondly, the initial path is produced by the basic grey wolf optimization (GWO) algorithm;and then, the fruit fly optimization (FOA) algorithm is used to continue the local optimization of the optimal solution;finally, the optimal path is generated. Compared with some other methods, the simulation results show that the improved grey wolf optimization algorithm is effective.

关键词： Oilfield inspection UAV Path planning Grey wolf optimization algorithm fruit fly optimization algorithm

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Multi-objective fruit fly optimization Based on Cloud Model 13

Multi-objective Fruit Fly Optimization Based on Cloud Model

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13th World Congress on Intelligent Control and Automation (WCICA)

作者： Wu, Lianghong Wang, Hui-Ying Zuo, Cili Wei, Hua-Liang Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9781538673454

A normal cloud model based fruit fly optimization algorithm is extended for multi-objective optimization. The normal cloud model is used to produce new food positions for the fruit flies in the osphresis-based search, and a parameter self-adaptive strategy is proposed to adjust the search space dynamically. The concept of Pareto domination is incorporated into the vision-based search and a cloud model based multi-objective fruit fly optimization algorithm (CMMOFOA) is then developed. To have a good diversity of the member in the elitist archive, a normalized nearest neighbor distance based diversity maintenance strategy is adopted. The OF test set in CEC2009 is used to test the performance of CMMOFOA. Numerical simulation results demonstrate that CMMOFOA can converge to the Pareto fronts of the test problems with good accuracy and distribution, and has better or competitive performance compared with six popular state-of-the-art algorithms.

关键词： fruit fly optimization algorithm Multi-objective optimization Normal cloud model

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A Modified fruit-fly optimization algorithm Aided PID Controller Designing

A Modified Fruit-Fly Optimization Algorithm Aided PID Contro...

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10th World Congress on Intelligent Control and Automation (WCICA)

作者： Liu, Yi Wang, Xuejie Li, Yanjun Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781467313988

fruit fly optimization algorithm (FOA) is one of the newest intelligent optimization algorithms. Attracted by its simple implement procedure with effective searching capability, our work is to popularize this algorithm to tackle some practical optimization applications requesting real-time performance. However, the updating strategy of FOA is with strong randomness, thus bringing in some blindness searching in solution updating, which will result in slow convergence rate and premature. Therefore, a odified FOA (MFOA) based on PSO and SA was proposed in this paper to improve the performance of basic FOA. Besides, Chaos funtion was used to enhance the stochastic and ergodic features of initial solution so as to improve the diversity of initial population in MFOA. PSO is introduced to reduce the blindness searching in solution updating. SA is used as a local search to improve the convergence rate. Finally, in order to verify the efficiency of MFOA algorithm, two common functions and a practical high-order AVR system with PID controller were tested in simulation. Experimental results revealed the encouraging performance of our proposed algorithm.

关键词： fruit fly optimization algorithm Particle Swarm optimization Simulated Annealing PID

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fruit fly algorithm Based on Extremal optimization 12

Fruit fly algorithm Based on Extremal optimization

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12th International Conference on Computational Intelligence and Security (CIS)

作者： Zhang, Shui-ping Chen, Yang Geng, Yang-dan Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Jiangxi Peoples R China

ISBN: (纸本)9781509048403

Aiming at the problem that solutions obtained by simply using FOA (fruit fly optimization algorithm) would fall into local optimum, with slow convergence and low accuracy, we therefore propose an algorithm EOFOA that combines EO (Extremal optimization algorithm) with FOA. EOFOA brings the basic idea of EO, an algorithm of extremal dynamics, into FOA, so as to prevent local optimum and increase convergence accuracy by taking advantages of changing the initial distribution strategy, improving the diversity of the fruit fly population through randomly replacing the individuals that are not adaptive to the population, and making use of its strong volatility that enables the algorithm to search continuously. Simulation results show that: EOFOA is more accurate than FOA and the convergence is also quickened, which is efficient in avoiding premature convergence.

关键词： fruit fly optimization algorithm Extremal optimization algorithm Premature convergence Local optimum

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A FOA-Optimized RBF algorithm-Based Evaluation Research on E-commerce Websites

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 2835-2851页

作者： Zhang, Xinhe Hu, Xia He, Chao Yu, Chunming Northwest Univ Sch Econ & Management Xian 710127 Shaanxi Peoples R China China Univ Min & Technol Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Management Xuzhou 221116 Jiangsu Peoples R China

By applying the Expert Grading Method and considering the features of e-commerce websites and properties of the indicators, this paper constructs a multi-indicator hierarchical structure for the competitiveness index evaluation of e-commerce website as well as built an indicator system for the evaluation. This system can be used to measure the competitiveness index of such a website and quantify its competitiveness. Then Radial Basis Function (RBF) Neural Network algorithm (NNA) is adopted to evaluate and research the competitiveness indexes of e-commerce websites. Against the problems therein, this paper tries to improve the RBF NNA with fruit fly optimization algorithm (FOA). Through the simulation and contrast of examples, FOA-RBF algorithm obviously works better than RBF NNA in measuring and evaluating the competitiveness indexes of such websites. Therefore, it is verified that the algorithm proposed by this paper is both effective and reliable.

关键词： E-commerce Neural network Radial Basis Function fruit fly optimization algorithm Expert Grading Method

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Mixed chaotic FOA with GRNN to construction of a mutual fund forecasting model

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COGNITIVE SYSTEMS RESEARCH 2018年 52卷 380-386页

作者： Han, Shi-Zhuan Huang, Li-Hui Zhou, Ying-Ying Liu, Zong-Li East China Jiaotong Univ Sch Econ & Management Nanchang Jiangxi Peoples R China Guangdong Univ Foreign Studies Sch Business Guangzhou Guangdong Peoples R China

This article attempts to collect the research data of mutual fund in Taiwan, evaluates the management performance of each fund by using the Data Envelopment Analysis and selects the mutual fund whose technical efficiency value is 1 as the investment goal. Then, this article optimizes the parameter of GRNN by using a variety of intelligence algorithms including AFSA, PSO and relatively new FOA, build the forecasting model of mutual fund net value and analyzes the prediction ability with GRNN model that is not optimized. After the compares of five prediction performance evaluation indicators, the model that optimizes the GRNN by using FOA has the highest prediction accuracy among four prediction models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Artificial intelligence Particle swarm optimization Artificial fish swarm algorithm fruit fly optimization algorithm Mutual fund

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Feed-forward and generalised regression neural networks in modelling feeding behaviour of pigs in the grow-finish phase

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BIOSYSTEMS ENGINEERING 2018年 173卷 124-133页

作者： Cross, Amanda J. Rohrer, Gary A. Brown-Brandl, Tami M. Cassady, Joseph P. Keel, Brittney N. South Dakota State Univ Dept Anim Sci Brookings SD 57007 USA ARS USDA US Meat Anim Res Ctr Clay Ctr NE 68933 USA

Feeding patterns of pigs have been investigated for use in management decisions and identifying sick animals. Development of models to predict feeding behaviour has been limited due to the large number of potential environmental factors involved and complex relationships between them. Artificial neural networks have been proven to be an effective tool for mapping complicated, nonlinear relationships between inputs and outputs. However, they have not been applied to feeding behaviour prediction. In this study, we compared the use of feed-forward (FFNN) and generalised regression neural networks (GRNN) in forecasting feeding behaviour of pigs in the grow-finish phase, using time of day and temperature humidity index as inputs. Models were calibrated on data from 1923 grow-finish pigs collected from 2008 to 2014, and their predictive ability was tested using data from four additional grow-finish groups collected from 2014 to 2016. Results indicated that FFNN trained with the Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) algorithms were the most accurate forecasting models. In three of the four validation groups, models trained with LM and SCG algorithms exhibited strong performance, with correlations between predicted and observed feeding behaviours ranging from 0.623 to 0.754. Large deviations between predicted and observed behaviours in the fourth validation group were probably the result of an outbreak of pneumonia, which demonstrates the potential for the model to be used in automated detection of disease outbreak and other stress events. This work is the first step in developing a fully automated system for detecting changes in feeding behaviour. Published by Elsevier Ltd on behalf of IAgrE.

关键词： Pigs Feeding behaviour Artificial neural network fruit fly optimization algorithm

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