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Unmanned Aerial Vehicle 3D Path Planning Based on an improved artificial fish swarm algorithm

DRONES 2023年第10期7卷

作者： Zhang, Tao Yu, Liya Li, Shaobo Wu, Fengbin Song, Qisong Zhang, Xingxing Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Guizhou Univ Coll Mech Engn Guiyang 550025 Peoples R China

A well-organized path can assist unmanned aerial vehicles (UAVs) in performing tasks efficiently. The artificial fish swarm algorithm (AFSA) is a widely used intelligent optimization algorithm. However, the traditional AFSA exhibits issues of non-uniform population distribution and susceptibility to local optimization. Despite the numerous AFSA variants introduced in recent years, many of them still grapple with challenges like slow convergence rates. To tackle the UAV path planning problem more effectively, we present an improved AFSA algorithm (IAFSA), which is primarily rooted in the following considerations: (1) The prevailing AFSA variants have not entirely resolved concerns related to population distribution disparities and a predisposition for local optimization. (2) Recognizing the specific demands of the UAV path planning problem, an algorithm that can combine global search capabilities with swift convergence becomes imperative. To evaluate the performance of IAFSA, it was tested on 10 constrained benchmark functions from CEC2020;the effectiveness of the proposed strategy is verified on the UAV 3D path planning problem;and comparative algorithmic experiments of IAFSA are conducted in different maps. The results of the comparison experiments show that IAFSA has high global convergence ability and speed.

关键词： UAV 3D path planning improved artificial fish swarm algorithm refractive opposition learning adaptive spiral search

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Optimization lighting layout of indoor visible light communication system based on improved artificial fish swarm algorithm

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JOURNAL OF OPTICS 2020年第3期22卷 035701-035701页

作者： Wang, Jiaan Xu, Ancheng Ju, Jintao Guo, Linyang Changzhou Inst Technol Sch Photoelect Engn Changzhou Jiangsu Peoples R China Changzhou Inst Technol Sch Elect & Informat Engn Changzhou Jiangsu Peoples R China Northern Laser Technol Grp Co Ltd Yangzhou Jiangsu Peoples R China

The layout of LED lamps plays an important role in indoor visible light communication systems, especially the uniformity of received optical power on the communication plane. In order to search an optimized layout that provides better communication performance, an improved artificial fish swarm algorithm (IAFSA) is proposed. In IAFSA, an artificial fish denotes the position of an LED that includes a pair of abscissa and ordinate. The order method based on exchanging list during the action of preying increases the population diversity, and adopt of an adaptive moving during the action of random move enhances the ability of global search and search efficiency, which ensures the convergence of IAFSA. Simultaneously, we establish objective function based on the mean square error of received optical power distribution for optimizing layout. Comparing the square layout, rectangular layout and circular layout with the IAFSA optimized layout, the power uniformity augments increases by 39.8%, 54.4% and 40.1%, respectively. The experiment results show that IAFSA is a feasible and valid intelligent optimization algorithm, and it can realize speedy optimized layout.

关键词： visible light communication optimization lighting layout improved artificial fish swarm algorithm

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Integrated optimization for mechanical elastic wheel and suspension based on an improved artificial fish swarm algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2019年 137卷 102722-000页

作者： Xu, Han Zhao, YouQun Ye, Chao Lin, Fen Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China

This paper aims to provide a baseline for the future optimum design of the Mechanical Elastic Wheel (MEW), and improve the ride comfort of the vehicle equipped with the MEW. A new factor delta is introduced, which is the ratio of the length of hinge groups to the thickness of elastic wheel. The influence of delta on the MEW radial stiffness is studied based on finite element analyses and experimental verification. The stiffness characteristics with delta are fitted by means of artificial neural network. A quarter-car model is established, combining the MEW and the hydro-pneumatic suspension. A multi-objective optimization is proposed, where an improved Pareto artificial fish swarm algorithm is applied to attain the appropriate parameters of both suspension and MEW. The results after optimization illustrate that the root mean square value of body vertical acceleration, suspension working space and dynamic tyre load are reduced by 43.88%, 24.38% and 46.46% respectively. In addition, the power spectral densities of three indexes are all dropped. This method proposed by this paper not only offers an optimal matching between the MEW and the hydro-pneumatic suspension, but also has the reference value for improving vehicle ride comfort.

关键词： Mechanical elastic wheel Radial stiffness Hydro-pneumatic suspension Ride comfort optimization improved artificial fish swarm algorithm

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Research on Aero-engine Maintenance Level Decision Based on improved artificial fish-swarm Optimization Random Forest algorithm

Research on Aero-engine Maintenance Level Decision Based on ...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Zhou, Yijiao Guo, Jilian Fu, Lingyun Liang, Tao Air Force Engn Univ Aeronaut Engn Coll Xian Shaanxi Peoples R China

ISBN: (纸本)9781538660577

Targeted at the problem of decision-making for aero-engine delivery and repair, a decision model of engine sending and repairing based on improved artificial fish swarm optimization is proposed. Firstly, aiming at the two key parameters mtry and ntree which affect the classification accuracy in random forest algorithm, a kind of artificial fish swarm optimization algorithm based on adaptive step and field of vision is used to optimize, and secondly, the decision model of engine delivery and repair is carried out on the basis of stochastic forest algorithm. Finally, the model is applied to the case study to classify the real data of a certain type of engine. The results show that the improved artificial fish swarm optimization algorithm is quick and effective, and the more training samples are, the higher the classification accuracy of the stochastic forest algorithm is, and the higher the classification accuracy is. The research can be used for reference to make decision-making in outfield engine, and has important practical significance.

关键词： improved artificial fish swarm algorithm Random forest Aero-engine Maintenance level

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Capacity Allocation of the Hybrid Wind-PV-Storage System Based on improved fish swarm Optimization algorithm

Capacity Allocation of the Hybrid Wind-PV-Storage System Bas...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Zhang, Yanni Shi, Jie Ma, Yan Fu, Zuan Univ Jinan Sch Phys Sci & Technol Jinan Peoples R China Shandong Inst Metrol Technol Innovat Ctr Atmospher Environm Monitoring Jinan Peoples R China Univ Jinan Sch Automat & Elect Engn Jinan Peoples R China

ISBN: (纸本)9798350339345

Hybrid Wind-PV-storage system is of great importance to increase solar and wind energy integration. In order to ensure its economic optimization based on continuous and reliable power supply, the artificial fish swarm algorithm based on improved parameters is applied to propose a model for optimizing energy storage unit capacity in the paper. The contribution of this paper is the life cycle cost (LCC) model, constructed to minimize the life cycle cost of Hybrid Wind-PV-Storage System based on the constraint, which is the loss of power supply probability (LPSP). The optimization model is applied to the Hybrid Wind- PV- Storage System in southern China. The optimization results are analyzed from the two aspects of both economy and reliability. The result shows that the life cycle cost optimized by the improved algorithm is reduced by 49.95% on average compared to the previous algorithm. In addition, the LPSP optimized results show better performance than that of the previous algorithm, which provides a reference to industrious solutions.

关键词： hybrid wind-PV-storage system improved artificial fish swarm algorithm life cycle cost the loss of power supply probability capacity optimization

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An artificial fish swarm algorithm for a multi-objective grain transportation problem

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EVOLUTIONARY INTELLIGENCE 2020年第1期13卷 9-19页

作者： Jia, Shiyu Zhou, Kang Zhou, Sisi Fang, Haocheng Zhen, Yiting Zou, Yilin Wuhan Polytech Univ Sch Math & Comp Wuhan 430023 Hubei Peoples R China

The problem of grain transportation optimization is a typical NP-complete problem. To solve the problem, it is necessary to construct a mathematical model for the optimization of grain transportation. As the single-objective grain transportation route optimization model is difficult to better simulate the complex and varied conditions in real life, the multi-objective grain transportation route optimization model is closer to reality and has more guiding significance for practical problems. Therefore, this paper constructs a multi-objective grain transportation optimization problem model. And improved the artificial fish swarm algorithm to make it can be better solution. First, a similar fragment distance is introduced to replace the traditional distance calculation method. Second, we play the guiding role of bulletin board to insert the optimal solution fragment in the bulletin board into the current solution. Finally, according to the characteristics of food transportation problems, three behaviors of artificial fish were improved and mixed neighborhood search was conducted. In simulation experiments, the precision of the traditional artificial fish algorithm and improved algorithm is more and more low with the increase of amount of data. The difference between that and the optimal solution in the database is becoming more and more big, but the error in not only path length but also the number of vehicles of the improved algorithm is still within the scope of the permit. The error of the traditional artificial fish algorithm is far beyond permissible range. Experimental results show that the improved artificial fish swarm algorithm achieves high solution accuracy in path length and the number of vehicles. However, because there is no time window constraint, the conflict between the number of vehicle and the path length is very small. Finally, the set of Pareto solutions converges to 1 or 2 points.

关键词： MOP GTOP artificial fish swarm algorithm improved artificial fish swarm algorithm

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Comprehensive Evaluation of Aero-engine Performance Based on Chaotic artificial fish-swarm Optimization algorithm

Comprehensive Evaluation of Aero-engine Performance Based on...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Zhou Yijiao Fu Lingyun Guo Jilian Jiang Yucheng Air Force Engn Univ Grad Sch Xian Shaanxi Peoples R China Air Force Engn Univ Aeronaut Engn Coll Xian Shaanxi Peoples R China Chinese Peoples Liberat Army 95959 Troops Beijing Peoples R China

ISBN: (纸本)9781538678619

Targeted at the problem that the performance parameters of aero-engine performance evaluation are difficult to determine, a comprehensive evaluation method of aero-engine performance based on chaotic artificial fish-swarm optimization algorithm is proposed. Based on the actual field guarantee, this paper collects and collates the performance parameter data of aero-engine normal and fault, and obtains the weights of each evaluation index by chaotic search and improved artificial fish swarm optimization algorithm, and compares the chaos search and the improved artificial fish swarm optimization algorithm with the basic artificial fish swarm algorithm and the artificial fish swarm algorithm of the adaptive step and the visual field. The results show that the algorithm based on chaotic artificial fish swarm optimization has fast convergence speed and higher precision. Finally, the model is applied to the case analysis, the performance of multiple engines is queued and the performance trend of single engine is analyzed, and the results show that the performance evaluation is more comprehensive and accurate, which can provide reference for engine performance queue and fault prevention and maintenance plan, and it has important practical significance.

关键词： chaos search improved artificial fish swarm algorithm aero-engine performance comprehensive evaluation

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Forecasting annual natural gas consumption via the application of a novel hybrid model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第17期28卷 21411-21424页

作者： Gao, Feng Shao, Xueyan Chinese Acad Sci Inst Sci & Dev Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

关键词： Support vector machine improved artificial fish swarm algorithm Natural gas consumption forecasting

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Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model

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SENSORS 2020年第15期20卷 4274页

作者： Kang, Juntao Zhang, Xueqiang Cao, Hongyou Qin, Shiqiang Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Peoples R China

Due to insufficient test data, insufficient constraint equations and uncertain objective function, the local optimal solution and the global optimal solution of the objective function in finite element model updating may represent the actual parameters of the structure. Based on this, this paper proposes an improved artificial fish school algorithm. By combining the niche technology with the artificial fish school algorithm, the improved algorithm can systematically find multiple global optimal solutions and local optimal solutions of the objective function. Aiming at the difficulty of determining the niche radius, an adaptive niche radius mechanism is proposed. The improved algorithm is used to study the multi-alternatives problem of finite element model updating after verifying its feasibility through numerical simulation analysis. In the case of benchmark framework model updating, it is confirmed that multi-alternative problems exist and the global optimal solution of the objective function does not necessarily represent the true parameters of the structure. In case 2, the improved algorithm combined with the Kriging model is applied to the model updating of a cable-stayed footbridge, and 15 sets of solutions are obtained, in which the error objective function values of the measured and theoretical values of the bridge modes are close but the solutions are completely different. Combining with the actual bridge condition and reanalysis technology, the author takes the suboptimal solution 2 as the most representative solution of the bridge parameters, which reduces the possibility of misjudgment of structural parameters.

关键词： finite model updating multi-alternatives problem improved artificial fish swarm algorithm Kriging model

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Reduction of Residual Vibrations in High-speed Cam Mechanisms Using Non-uniform Rational B-splines

Reduction of Residual Vibrations in High-speed Cam Mechanism...

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2nd International Conference on Machinery, Materials Science and Engineering Applications (MMSE 2012)

作者： Xuan, Guantao Shao, Yuanyuan Lu, Zhaoqin Shandong Agr Univ Shandong Prov Key Lab Hort Machineries & Equipmen Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9783037854099

In high-speed cam mechanisms, the output motion and positioning accuracy are affected greatly by residual vibrations. To reduce residual vibration, non-uniform rational B-splines (NURBS) is employed to reconstruct the cam profile. Based on elastic dynamic theory, this paper designs the multi-objective dynamic optimization model of high-speed cam mechanisms, and proposes an improved artificial fish swarm algorithm (IAFSA) to optimize the cam profile. Utilizing the taboo search strategy, NURBS profile is optimized twice with variable parameters. By adjusting parameters of NURBS, dynamic characteristics of the follower motion curve are improved, and the residual vibration is reduced. Finally, an optimization simulation example is given to demonstrate the usefulness and effectiveness of the approach.

关键词： Residual vibration Non-uniform rational B-splines Profile optimization improved artificial fish swarm algorithm

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