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State prediction of hydro-turbine based on WOA-RF-Adaboost

ENERGY REPORTS 2022年 8卷 13129-13137页

作者： Lan, Chaofeng Song, Bowen Zhang, Lei Fu, Lirong Guo, Xiaoxia Sun, Chao Harbin Univ Sci & Technol Sch Measurement & Commun Engn Harbin Peoples R China Beidahuang Ind Grp Gen Hosp Harbin 150088 Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Hainan Peoples R China

Aiming at the problem that the prediction performance of remote operation state of hydro-turbine needs to be improved, this paper uses whale optimization algorithm (WOA) to optimize random forest (RF). It combines it with Adaboost algorithm to propose the prediction model in this paper. Firstly, the signal of hydro-turbine is analyzed by variational modal decomposition (VMD), and the penalty factor and the number of IMF components K of VMD are optimized by fruit fly optimization algorithm (FOA). The arrangement entropy of intrinsic mode function (IMF) components, kurtosis, mean value of original signal are calculated, and the input eigenvector of hydro-turbine state prediction model is constructed;Secondly, the number of split attribute sets and the optimal number of decision trees of RF are optimized by WOA, and multiple WOA-RF models are iteratively trained by Adaboost algorithm to construct WOA-RF-Adaboost state prediction model. The prediction effect of the proposed model and the traditional model is evaluated by correct rate and confusion matrix. The results show that the prediction accuracy of WOA-RF-Adaboost model proposed in this paper is 99.2%, it has good state prediction performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Hydro-turbine State prediction Variational modal decomposition fruit fly optimization algorithm Whale optimization algorithm Random forest

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Research on Impedance Control Based on Improved RBF Neural Network 24

Research on Impedance Control Based on Improved RBF Neural N...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Duan, Qingjuan Jiang, Guicheng School of Mechano-Electronic Engineering Xidian University China

ISBN: (纸本)9798400717192

Aiming at the problem of robot compliance control in unknown environment, an impedance control method based on improved RBF neural network (RBFNN) is proposed in this paper. The learning rates, momentum factors and weights of the neural network are adjusted by the fruit fly optimization algorithm (FOA) to improve the convergence speed and optimization performance of the neural network. Based on the improved neural network, an impedance controller is designed, which can adjust the impedance parameters in real time to achieve better compliance control effect. The simulation and experimental results show that the controller can effectively reduce the force control error and apply to a wider range of environments than the traditional impedance control and adaptive impedance control. © 2024 ACM.

关键词： fruit fly optimization algorithm

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Network Security Situation Assessment Based on PCA-IFOA-PNN 3

Network Security Situation Assessment Based on PCA-IFOA-PNN

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Wang, Huan Guo, Lejiang Lu, Hao Gao, Jingxiang Teaching and Research Support Center Air Force Early Warning Academy Wuhan China Air Force Early Warning Academy Department of Early Warning Technology Wuhan China

ISBN: (纸本)9798350359961

At present, all kinds of trained neural networks have been widely used in network security situation assessment, but there are some problems such as slow convergence speed and low assessment accuracy. Aiming at the problem of network security situation assessment based on the existing neural network, a network security situation assessment method based on improved probabilistic neural network is proposed. Firstly, the original collected network situation data is processed, and the principal component analysis (PCA) is used to reduce the data dimension. Secondly, the improved fruit fly optimization algorithm (IFOA) is used to optimize the smoothing factor of probabilistic neural network (PNN). Finally, the network security situation is assessed by the optimized PNN. Experiments demonstrate that the proposed method is more accurate than the traditional neural network for network security situation assessment, and the training speed is faster. © 2023 IEEE.

关键词： fruit fly optimization algorithm network security situation principal component analysis probabilistic neural network

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A Hybrid Gain-Ant Colony algorithm for Green Vehicle Routing Problem 9

A Hybrid Gain-Ant Colony Algorithm for Green Vehicle Routing...

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9th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Sangeetha, V. KrishanKumar, R. Ravichandran, K. S. Gandomi, Amir H. Amrita Vishwa Vidyapeetham TIFACCORE Cyber Secur Amrita Sch Engn Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Coimbatore Tamil Nadu India Univ Technol Sydney Fac Engn & IT Sydney NSW Australia

ISBN: (纸本)9798350320886

Increasing carbon emissions, and thus footprint, is one of the main reasons for the imbalance in environmental sustainability, which is primarily contributed to transportation. Transportation is a core functionality of logistics distribution and supply chain. In this paper, a hybrid gain-ant colony optimization and fruit fly optimization algorithm for green vehicle routing problem is proposed to plan shortest paths with reduced total fuel consumption efficiently. The proposed algorithm was simulated using the Erdogan and Miller Hooks dataset and compared with best- known solutions and existing methods.

关键词： ant colony optimization fruit fly optimization algorithm green vehicle routing problem pheromone gain sustainability

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Solving VRPTW Problem Based on Improved Drosophila optimization algorithm 6

Solving VRPTW Problem Based on Improved Drosophila Optimizat...

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6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Biao, Pang Qiu, Zheng Ru Jie, Luo Sheng COSL Geophys R&D Inst Data Ctr zhanjiang Zhanjiang Peoples R China

ISBN: (纸本)9781665454889

Aiming at the defects of premature convergence, local poor investigation ability and difficulty in obtaining the best path in traditional swarm intelligent transportation algorithm, an improved Drosophila optimization algorithm is proposed. In this method, the optimization goal is to minimize the minimum delivery distance. A Drosophila optimization algorithm based on step exponential reduction strategy and distribution perturbation strategy can minimize the distribution distance and optimize the target. In the mutation process, the reverse selection mechanism of the roulette wheel is used, and the optimal value after perturbation is selected as the basis for step size change, so as to maintain the diversity of the fruit fly population, so that the algorithm maintains a good optimization ability and further enhances the search ability. Finally, the simulation experiment on Matlab shows that the algorithm can not only improve the overall optimization effect, but also has strong stability and is not easy to be disturbed. It can provide a new method to solve the VRPTW problem.

关键词： path planning fruit fly optimization algorithm exponential decreasing roulette strategy VRPTW problem

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Self-tuning neural network generalized predictive control and energy consumption optimization for electro-hydraulic composite steering system of commercial vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Chen, Xiang Hou, Kailong Zhao, Wanzhong Wang, Chunyan Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

Heavy commercial vehicles equipped with traditional power steering system are inadequate to balance steering portability, steering stability, and fuel economy. Therefore, an electro-hydraulic composite steering (EHCS) system that integrates electric power steering (EPS) system and electro-hydraulic power steering (EHPS) system has been proposed. However, since the system has two actuators with different characteristics, how to effectively track assistance, reduce steering torque fluctuations, and further reduce energy usage has become a focus. This paper proposes a self-tuning neural network generalized predictive controller based on improved fruit fly optimization algorithm to address the above issues. Firstly, the dynamic model is derived through an analysis of the structure and dynamic properties of the EHCS system. To address the problem of energy usage, this paper develops a double-swarm division and cooperation fruit fly optimization algorithm (DDCFOA) to achieve optimal distribution of steering assist torque. The neural network implicit stair-like generalized predictive controller (NNISGPC) is designed to control the output assistance of a nonlinear time-delay EHPS system, and the issue of steering torque fluctuations caused by hysteresis is addressed through the adoption of electric power compensation control. Simultaneously, using DDCFOA to optimize the neural network and achieve controller parameters self-tuning. Finally, a joint simulation verification is conducted with AMEsim and Simulink. The results indicate that the suggested approach can effectively enhance the tracking performance of assist control, reduce steering torque fluctuations, and minimize energy usage.

关键词： Electro-hydraulic composite steering neural network implicit stair-like generalized predictive controller parameters self-tuning fruit fly optimization algorithm

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A new combined braking approach of magnetorheological fluids brake based on fuzzy controller and improved PID controller

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第23期235卷 6644-6658页

作者： Hua, Dezheng Liu, Xinhua Gupta, Munish Kumar Fracz, Pawel Li, Zhixiong China Univ Min & Technol Sch Mechatron Engn Xuzhou Jiangsu Peoples R China Technol & Innovat Res Ctr JiangYan EDZ Taizhou Peoples R China Shandong Univ Sch Mech Engn Jinan Shandong Peoples R China Opole Univ Technol Dept Mfg Engn & Automat Prod Opole Poland Ocean Univ China Sch Engn Qingdao Shandong Peoples R China Yonsei Univ Yonsei Frontier Lab 50 Yonsei Ro Seoul 03722 South Korea

With the aim to improve working performance of magnetorheological fluids (MRF) brake, a combined braking approach is proposed in this paper. A data acquisition system of MRF brake is designed and transfer function is identi?ed for the device. Furthermore, based on the excitation current-extrusion pressure combined braking system, a fuzzy controller is designed to distribute the target torque. Moreover, through integration of fruit fly optimization algorithm (FOA) and particle swarm optimization algorithm (PSOA), an improved proportional-integral-differential (PID) controller is presented to conduct braking mechanism. Finally, the simulations and experiments are carried out. The results show that the combined braking approach with fuzzy controller and improved PID controller has characteristics of quick response and no overshoot, and superior to conventional PID and FOA PID controller.

关键词： Combined braking system magnetorheological fluids brake fuzzy controller PID controller fruit fly optimization algorithm

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A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第8期29卷 1773-1792页

作者： Seghir, Fateh Khababa, Abdellah Univ Ferhat Abbas Setif 1 Dept Comp Sci Setif 19000 Algeria

This paper addresses the QoS-aware cloud service composition problem, which is known as a NP-hard problem, and proposes a hybrid genetic algorithm (HGA) to solve it. The proposed algorithm combines two phases to perform the evolutionary process search, including genetic algorithm phase and fruit fly optimization phase. In genetic algorithm phase, a novel roulette wheel selection operator is proposed to enhance the efficiency and the exploration search. To reduce the computation time and to maintain a balance between the exploration and exploitation abilities of the proposed HGA, the fruit fly optimization phase is incorporated as a local search strategy. In order to speed-up the convergence of the proposed algorithm, the initial population of HGA is created on the basis of a heuristic local selection method, and the elitism strategy is applied in each generation to prevent the loss of the best solutions during the evolutionary process. The parameter settings of our HGA were tuned and calibrated using the taguchi method of design of experiment, and we suggested the optimal values of these parameters. The experimental results show that the proposed algorithm outperforms the simple genetic algorithm, simple fruit fly optimization algorithm, and another recently proposed algorithm (DGABC) in terms of optimality, computation time, convergence speed and feasibility rate.

关键词： Service composition Cloud computing Quality of service (QoS) Genetic algorithm fruit fly optimization algorithm

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A bioinformatic variant fruit fly optimizer for tackling optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 213卷 106704-106704页

作者： Fan, Yi Wang, Pengjun Mafarja, Majdi Wang, Mingjing Zhao, Xuehua Chen, Huiling Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Birzeit Univ Dept Comp Sci Birzeit Palestine Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

The fruit fly optimization algorithm (FOA) is a swarm-based algorithm inspired by fruit flies' food search behaviors in nature. The conventional FOA is attracting widespread interests due to its briefness, and simplicity in structure. However, FOA still has some disadvantages presented in the exploration and exploitation abilities when it is used to solve different optimization problems. To optimize these drawbacks, the performance FOA can be improved by employing different operators that can help it explore more promising areas in the search space and in finding better solutions in the local area of the candidate optimal solutions. In this paper, an improved FOA approach (called BSSFOA) that employs (1) bat sonar strategy to strengthen the exploration, (2) hybrid distribution that combined Gaussian distribution with student distribution to enhance the exploitation is proposed. In BSSFOA, the FOA uses the bat sonar strategy to search for the global optima, while the hybrid distribution mechanism is used to search the local area of the global optima in the hope of finding better solutions. To assess the performance of the proposed approach, a comprehensive set of 30 benchmark functions was used with the continuous version of the BSSFOA. Moreover, a discrete version of BSSFOA was proposed as a searching mechanism in the feature selection process, where 17 well-known datasets were used to assess the ability of the BSSFOA to search the best performing features among these datasets. The obtained results reveal the superiority of the BSSFOA in solving both continuous and discrete optimization problems. Therefore, it can be concluded that the employed mechanisms have constructive impacts in mitigating the core problems of FOA. (C) 2020 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Swarm intelligence Global optimization Feature selection

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Role Mechanism of Enterprise Management Efficiency Improvement Based on Improved Drosophila algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第6期10卷

作者： Wang, Hui Shandong Elect Power Engn Consulting Inst Corp Ltd Jinan 250100 Shandong Peoples R China

INTRODUCTION: With today's intelligent technology in full swing, many companies and enterprises need to catch up to the reality of internal management. To make the company better adapt to society and realize its sustainable development, it is essential to optimize the internal management means and improve the management efficiency of the enterprise. OBJECTIVES: To find the optimal staff allocation scheme and the best decision path by utilizing the improved fruit fly algorithm and establishing the enterprise's regular task and staff allocation model. METHODS: We analyze the standard swarm intelligence algorithms, and then we compare the differences between the basic fruit fly algorithm, the optimized fruit fly algorithm, and the above swarm intelligence algorithms. The fruit fly algorithm is utilized, and the algorithm is optimized to fit the actual enterprise management model. At the same time, the influence of Levy's flight on the Drosophila algorithm in enterprise management efficiency improvement is studied. Finally, it points out the application fields, the optimized Drosophila algorithm's current situation, and the existing problems and shortcomings. RESULTS: By comparing the performance of the Drosophila algorithm with that of the other three optimization Drosophila algorithms, the influence factors of Levy flight on the enterprise's internal management allocation are obtained, making the algorithm a good fit for the model. The results of the optimal allocation scheme under known conditions and the best decision were obtained. CONCLUSION: The experimental results show that the optimized fruit fly algorithm can solve the multi-parameter allocation problem of enterprise management, which has high reference value and significance for general enterprise internal management.

关键词： fruit fly optimization algorithm business management efficiency improvement management innovation

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