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检索条件"主题词=Fruit fly optimization algorithm"

共 248 条记录，以下是211-220 订阅

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Coal and Gangue Recognition based on Improved Support Vector Machine

Coal and Gangue Recognition based on Improved Support Vector...

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International Conference on Computer Vision, Application, and Design (CVAD)

作者： Sun, Yu Xie, Yehui Zhang, Jincai Xian Univ Sci & Technol Xian Peoples R China

ISBN: (数字)9781510651876

ISBN: (纸本)9781510651876;9781510651869

In the process of coal mining, the separation of coal and gangue is a very important step. Traditional coal preparation methods include manual coal preparation, heavy medium coal preparation, ray projection coal preparation, etc. these methods can not separate coal and gangue under the condition of safety and speed at the same time. Therefore, to improve the recognition rate of coal gangue separation, a coal gangue recognition method based on improved Support Vector Machine is proposed in this paper. First, the images of the coal and gangue are preprocessed. Then, the gray and texture features of the coal and gangue are extracted from the preprocessed images. Finally, each feature vector is input into the Support Vector Machine model optimized by fruit fly for recognition and classification. The experimental results show that the accuracy is 96.33%.

关键词： Support Vector Machine Coal and Gangue Recognition fruit fly optimization algorithm

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Using binary fruit fly algorithm for solving the Set Covering Problem 10

Using binary Fruit Fly algorithm for solving the Set Coverin...

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10th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Crawford, Broderick Soto, Ricardo Torres-Rojas, Claudio Pena, Cristian Riquelme-Leiva, Marco Johnson, Franklin Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Finis Terrae Santiago Chile Univ San Sebastian Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cent Chile Santiago Chile Univ Playa Ancha Valparaiso Chile Univ Diego Portales Santiago Chile

ISBN: (纸本)9781479983308

Many practical applications are used in set covering problems (SCP), in this research, we used to solve SCP: the binary fruit fly optimization algorithms. This algorithm is divided in four phases: initiation, smell based search local vision based search and global vision based search. The metaheuristic is based by the knowledge from the foraging behavior of fruit-flies in finding food. The algorithm used a probability vector to improve the exploration. The tests were performed with eight different transfer functions and an elitist selection method. The test results show the effectiveness of the algorithm proposed.

关键词： fruit fly optimization algorithm set covering problem transfer functions metaheuristics

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Prediction of the Vertical Roller Mill Key Parameters Based on Optimized ESN algorithm 5

Prediction of the Vertical Roller Mill Key Parameters Based ...

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5th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Long, Tao Fang, Yilin Liu, Quan Liu, Aiming Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Minist Educ Key Lab Fiber Opt Sensing Technol & Informat Proc Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781509021291

The prediction of some key parameters in the operational process of vertical roller mill is very important for its safety and reliability. Because the vertical mill works too long in poor conditions, its key operating parameters are nonlinear and time-varying and the traditional prediction methods are difficult to achieve high accuracy. This paper selects the upper shell vibration signal of the vertical mill for the research. The signal processing has three steps. The first step is to remove the noise of the signal by using ensemble empirical mode decomposition (EEMD);the second step is to introduce the echo state network (ESN), which is a very suitable method for predicting in nonlinear chaotic system;the last step is to optimize the key parameters of the reserve pool in echo state network using fruit fly optimization algorithm (FOA) and the echo state network prediction model based on fruit fly optimization algorithm is put forward. The simulation results show the improved prediction model has higher prediction accuracy compared with the method without optimization, which proves the improved prediction model is suitable for the prediction on the upper shell vibration signal of the vertical mill.

关键词： Vertical roller mill Ensemble empirical mode decomposition Echo state network fruit fly optimization algorithm

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Research on Failure Prognostics Method of Electronic System Based on Improved fruit fly algorithm and Grey Fast Relevance Vector Machine 8

Research on Failure Prognostics Method of Electronic System ...

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8th International Conference on Intelligent Networking and Collaborative Systems (INCoS)

作者： Wu, Kun Kang, Jianshe Wang, Xuan Mech Engn Coll Dept Equipment Command & Management Shijiazhuang Peoples R China Engn Univ China Armed Police Force Dept Elect Technol Xian Peoples R China

ISBN: (纸本)9781509041237

In order to solve the failure prognostics problem of electronic system, a method of fast relevance vector machine (FRVM) based on improved fruit fly optimization algorithm (FOA) is proposed. Grey data generation operation is introduced to process the original data and the output data for enhancing the regularity and reducing the randomness. Furthermore, the kernel function parameter of FRVM model is optimized based on the improved FOA which adds the annealing parameter to establish the prediction model. In addition, the performance of the proposed model is studied and evaluated by a radar transmitter fault prediction experiment in this paper. The results demonstrate that the presented method performs significantly better than the traditional methods in terms of global optimization, convergence speed, training time and prediction accuracy.

关键词： electronic system grey theory fruit fly optimization algorithm fast relevance vector machine failure prognostics

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A channel assignment algorithm of CRSNs based on FOA 12

A channel assignment algorithm of CRSNs based on FOA

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Huang, Xiaomu Du, Jiayi Kuang, Shijie Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Hunan Univ Natl Comp Ctr Changsha Changsha Hunan Peoples R China

ISBN: (纸本)9781509040933

Cognitive radios is popular to solve the spectrum scarcity problem. In this paper, we investigate the channel assignment problem in the cognitive radio sensor networks. Due to the inherent power and resource constraints of wireless sensor networks, energy efficiency is the primary concern for networks design. Firstly, a model of data transmission and channel state is described to estimate the predicted residual energy. Secondly, we propose a FOA-based channel assignment. Lastly, we compare our algorithm with other two optimization-based channel assignment algorithms with the proposed method. Simulations results show that FOA-based channel assignment performances with the other two algorithms with respect to residual energy.

关键词： Cognitive Radio Sensor Networks fruit fly optimization algorithm Channel Assignment

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Prediction Model of Dissolved Oxygen Based on FOA-LSSVR 36

Prediction Model of Dissolved Oxygen Based on FOA-LSSVR

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第36届中国控制会议

作者： Chengyun Zhu Xingqiao Liu Wangfang Ding School of Electrical and Information Engineering Jiangsu University School of New Energy and Electronic Technology Yancheng Teachers University National Engineering and Technology Center for Information Agriculture Nanjing Agriculture University

ISBN: (纸本)9781538629185

Dissolved oxygen directly affects the growth status of fishes in intensive aquaculture,thus we set up a prediction model to determine the future changing trend of dissolved *** dissolved oxygen prediction model we proposed was based on the least squares support vector regression（LSSVR） model with fruit fly optimization algorithm（FOA） to find optimal parameters（γ and σ） of *** these two parameters can significantly affect the performance of the LSSVR,we studied the other two parameter optimization methods the particle swarm optimization（PSO） algorithm,the genetic algorithm（GA） and immune genetic algorithm（IGA） to compare them with the FOA *** calculated mean absolute percentage errors of the results of the four prediction models were 0.35%,1.3%,2.03% and 1.33%,*** FOA-LSSVR model has a higher prediction accuracy and more reliable performance than the other *** the predicted values of dissolved oxygen fall below the safety level,the farmer can start an oxygen increasing machine in advance to maintain the safety of *** prediction model was used in Yangzhong,Jiangsu province,China,and it performed well and helped farmers make decisions and reduce aquaculture risks.

关键词： Dissolved oxygen prediction model least squares support vector machines fruit fly optimization algorithm

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A Two-stage algorithm for Fuzzy Online Order Dispatching Problem

A Two-stage Algorithm for Fuzzy Online Order Dispatching Pro...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Zheng, Jie Wang, Shengyao Wang, Ling Chen, Jing-fang Wang, Li Hao, Jinghua He, Renqing Sun, Zhizhao Tsinghua Univ Dept Automat Beijing Peoples R China Meituan Dianping Grp Beijing Peoples R China

ISBN: (纸本)9781728169293

Considering the uncertainty in the real world online food delivery applications, this paper addresses an online order dispatching problem with fuzzy preparation times (FOODP). According to the characteristics of the problem, the FOODP is decomposed into two sub-problems, an order assignment problem and a fuzzy traveling salesman problem with pickup and delivery. To deal with the two sub-problems efficiently, a two-stage algorithm is proposed by reasonably fusing a modified greedy search (mGS) and the fruit fly optimization algorithm (FOA). In the mGS phase, agreement index is employed in the multi-stage decision to search for robust optimal solutions. In the FOA-based search phase, a modified heuristic is used to generate the initial route. To enhance the exploration of the algorithm, an olfactory search is designed by the cooperation of several problem-specific search operators. Moreover, a specific local intensification is employed to further improve the performance of solutions. Numerical tests and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： fuzzy online order dispatching problem the two-stage algorithm modified greedy search heuristic fruit fly optimization algorithm robustness

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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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Feature Selection using Dynamic Binary Particle Swarm optimization for Arabian Horse Identification System 12

Feature Selection using Dynamic Binary Particle Swarm Optimi...

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12th International Conference on Computer Engineering and Systems (ICCES)

作者： Zekrallah, Samar I. Soliman, Mona M. Hassanien, Aboul Ella Cairo Univ Informat Technol Dept Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781538611913

Being able to formally identify horses is crucial for many reasons like biosecurity and regulatory risks, fairness in competition to ensure the proper horse and owner are competing in an event, retrieval after theft and medical record management. Among different horses types Arabian horse is one of the top ten popular horse breeds all over the world. It is the most expensive horse in the market. Such horse identification system can be based on biometric parameters. This work aims to introduce a novel method of periocular region segmentation using Otsu based method in combination with Improved fruit fly optimization algorithm (IFOA) followed by a feature extraction and selection phase for Arabian horse identification system. The segmented horse periocular region is subjected to texture analysis using Gabor filter and discrete cosine transform for proper feature extraction. A proper Feature Selection step is performed with the aim of selecting optimum features. Such optimal set of features will be used later in Arabian horse identification and recognition system. Such optimal feature selection is achieved using Dynamic Binary Particle Swarm optimization.

关键词： periocular region Biometric identification iris recognition Arabian horse identification Otsu thresholding fruit fly optimization algorithm Dynamic Binary Particle Swarm optimization

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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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