检索结果-内蒙古大学图书馆

BEFA: bald eagle firefly algorithm enabled deep recurrent neural network-based food quality prediction using dairy products

引用

International Journal of Wireless and Mobile Computing 2024年第4期27卷 320-334页

作者： Manisha, Noothi Jagadeeshwar, Madiraju Department of Computer Science Chaitanya Deemed to be University Urban Telangana Warangal506001 India

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%. © 2024 Inderscience Enterprises Ltd.

关键词： DNN deep neural network DRNN deep recurrent neural network BES bald eagle search algorithm firefly algorithm min-max normalisation

来源：评论

学校读者我要写书评

暂无评论

Investigation of transmission line operation condition monitoring method based on firefly algorithm

引用

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第4期18卷 3129-3142页

作者： Zhao, Mingguan Li, Meng Dong, Xinsheng Yang, Yang Wang, Hongxia Ni, Yunlong State Grid Xinjiang Elect Power Res Inst Urumqi 830011 Xinjiang Peoples R China Xinjiang Key Lab Extreme Environm Operat & Detect Urumqi Xinjiang Peoples R China Kashgar Power Supply Co State Grid Xinjiang Elect Power Co Ltd Kashgar Xinjiang Peoples R China

With the explosive growth of electricity consumption, the demand for electricity by electricity users is increasing. As a core component of power supply, the safe and stable operation of transmission lines plays an important role in the normal operation of the entire power system. However, traditional monitoring methods for transmission line operation status face challenges such as limited accuracy, lack of real-time feedback, and high operational costs. In this paper, the firefly algorithm is used to monitor the running status of transmission lines. Through synchronous testing with the traditional particle swarm optimization algorithm, it is found that the average accuracy of the firefly algorithm in voltage and current measurement is improved to 93.13% and 93.66% respectively, which is better than the traditional algorithm. firefly algorithm shows high precision in various power equipment monitoring, the average monitoring accuracy is 95.62% and 93.06%, respectively, which proves that it has stronger performance in transmission line monitoring and can achieve more stringent monitoring requirements. Through the comparison experiment of the algorithm, it proved that the firefly algorithm had a strong performance in the transmission line operation status monitoring, and could more accurately identify the transmission line fault, which provided a new idea and new method for the safe operation status monitoring of transmission lines.

关键词： Transmission line firefly algorithm state detection particle swarm algorithm power system

来源：评论

学校读者我要写书评

暂无评论

Efficient feedback linearization control for an IPMSM of EVs based on improved firefly algorithm

引用

ISA TRANSACTIONS 2023年 134卷 431-441页

作者： Sun, Xiaodong Xu, Naixi Yao, Ming Cai, Feng Wu, Minkai Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automobile & Traff Engn Zhenjiang 212013 Peoples R China

The conventional direct torque control (DTC) has high torque and stator flux fluctuation that causes the stator current distortion. This paper presents an efficient control method based on the feedback -linearization direct torque control (FL-DTC) method for an interior permanent magnet synchronous motor (IPMSM) drive by using an improved firefly algorithm. The proposed approach can greatly restrain the poor performance of torque and stator flux. Thus, it is suitable for IPMSM drives in electric vehicles. First, a decoupled linear model is derived to implement the proposed efficient feedback linearization control for the IPMSM. Two phase voltages in d-q axes and two additional control inputs take shape into an isomorphism mapping with the concept of orthogonal transformation. The torque generation is related to the additional control. Second, the Hamiltonian efficient control theory combined with an improved firefly algorithm is applied to obtain an analytical solution. An efficient linearization controller is designed with a cost function considering the maximum voltage of the inverter. Finally, simulation and experiment are carried out to compare the performance of the proposed efficient FL-DTC with the improved firefly algorithm and the conventional direct torque control. The results show that the proposed control method can reduce the torque and flux ripples at a steady state and maintains a good dynamic response with the variations of speed and torque.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Interior permanent magnet synchronous motor (IPMSM) Feedback linearization control Direct torque control firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved firefly algorithm for feature selection with the ReliefF-based initialization and the weighted voting mechanism

引用

NEURAL COMPUTING & APPLICATIONS 2023年第1期35卷 275-301页

作者： Yong, Xin Gao, Yue-lin North Minzu Univ Sch Comp Sci & Engn Wenchang North St Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Key Lab Intelligent Informat & Data Wenchang North St Yinchuan 750021 Ningxia Peoples R China

Feature selection has become popular in data mining tasks currently for its ability of improving the performance of the algorithm and gaining more information about the dataset. Although the firefly algorithm is a well-performed heuristic algorithm, there is still much room for improvement as to the feature selection problem. In this research, an improved firefly algorithm designed for feature selection with the ReliefF-based initialization method and the weighted voting mechanism is proposed. First of all, a feature grouping initialization method that combines the results of the ReliefF algorithm and the cosine similarity is designed to take place of random initialization. Then, the direction of the firefly is modified to move toward the optimal solution. Finally, inspired by the ensemble algorithm, a weighted voter is proposed to build recommended positions for fireflies, which is also integrated with the elite crossover operator and the mutation operator to improve the diversity of the population. Selected from the mixed swarm, a new population is constructed to replace the original population in the next stage. To verify the effectiveness of the algorithm proposed in this paper, 18 datasets are utilized and 9 comparison algorithms (e.g., Black Hole algorithm, Grey Wolf Optimizer and Pigeon Inspired Optimizer) from state-of-the-art related works are selected for the simulating experiments. The experimental results demonstrate the superiority of the proposed algorithm applied to the feature selection problem.

关键词： firefly algorithm Feature selection Feature grouping ReliefF algorithm Weighted voting

来源：评论

学校读者我要写书评

暂无评论

An integrated firefly algorithm for the optimization of constrained engineering design problems

引用

SOFT COMPUTING 2024年第4期28卷 3207-3250页

作者： Tao, Ran Zhou, Huanlin Meng, Zeng Liu, Zhaotao Hefei Univ Technol Sch Civil Engn Hefei 230009 Anhui Peoples R China

As a nature-inspired metaheuristic method, the firefly algorithm (FA) arises more attentions in academic and engineering fields. However, too much attraction in FA's global attraction model leads to low computational efficiency, and the stochastic model with fixed randomization parameter is hard to balance the exploitation and exploration of the algorithm. Thus, FA still needs improvement to deal with complex engineering problems. An integrated firefly algorithm (IFA) that combines two novel attractive models with a new stochastic model is proposed to improve the standard FA. Firstly, the attractive model and stochastic model of standard FA are investigated through theoretical analysis and numerical experiments. And the factors that affect the computational efficiency and accuracy of FA are revealed. Based on the analysis results, two new fitness-based update formulas for attractiveness parameter are constructed to avoid the invalidation. The proposed virtual attractive model and global best attractive model can reduce the computation complexity and enhance the exploitation ability. Moreover, an adaptive strategy is presented for the stochastic model to achieve a better balance between exploitation and exploration. The nonlinearly decreased model for the update of parameter alpha can adjust the population diversity through the iteration and ensure the convergence. Additionally, an adaptive penalty function method is developed to handle the constraints effectively. Then, the initial parameters are tested, and the best initial parameters corresponding to the optimal performance of IFA are obtained. The proposed algorithm is evaluated by CEC2015 hybrid composition and a set of classical functions. The numerical experimental results show that the proposed techniques can enhance the solution accuracy and accelerate the convergence speed. Finally, IFA and other metaheuristic algorithms are applied to solve five engineering design optimization problems with mixed variables

关键词： firefly algorithm Attractive model Stochastic model Adaptive penalty function Engineering design optimization

来源：评论

学校读者我要写书评

暂无评论

firefly algorithm for Beamforming Design in RIS-aided Communication Systems 97

Firefly Algorithm for Beamforming Design in RIS-aided Commun...

引用

97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Le, Tuan Anh Yang, Xin-She Middlesex Univ Fac Sci & Technol London NW4 4BT England

ISBN: (纸本)9798350311143

This paper studies a non-convex power minimization problem for reconfigurable-intelligent-surfaces-aided communication systems whose constraints are multivariate functions of two independent optimization variables, i.e., active and passive beamforming vectors. A widely adopted alternative optimization (AO) approach approximates the originally non-convex problem by two convex sub-optimization problems where each suboptimization problem deals with one variable considering the other variable as a constant. The solution for the original problem is obtained by iteratively solving these sub-optimization problems. Although the AO approach converts the original NP-hard optimization problem to two convex sub-problems, the solutions attained by this method may not be the global optimal solution due to the approximation process as well as the inherent non-convexity of the original problem. To overcome the issue, this paper adopts a nature-inspired optimization approach and introduces a novel firefly algorithm (FA) to simultaneously solve for two independent optimization variables of the originally nonconvex optimization problem. Computational complexity analyses are provided for the proposed FA and the AO approaches. Simulation results reveal that the proposed FA approach prevails its AO counterpart in obtaining a better solution for the under-studied optimization problem with the same order of computational complexity.

关键词： firefly algorithm nature inspired optimization transmit beamforming reconfigurable intelligent surfaces

来源：评论

学校读者我要写书评

暂无评论

Optimal sitting and sizing of shunt capacitor for real power loss reduction on radial distribution system using firefly algorithm: A case study of Nigerian system

引用

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第2期45卷 5776-5788页

作者： Olabode, E. O. Ajewole, T. O. Okakwu, I. K. Ade-Ikuesan, O. O. Ladoke Akintola Univ Technol Dept Elect & Elect Engn Ogbomosho Nigeria Osun State Univ Dept Elect & Elect Engn Osogbo Nigeria Univ Benin Dept Elect & Elect Engn Benin Nigeria Olabisi Onabanjo Univ Dept Comp & Elect Engn Ago Iwoye Nigeria

Geometrical increase in power demand and high load density at the distribution ends of modern power systems have key consequential problems of high power loss and poor voltage profile, as a result of which the integrity of radial distribution networks to faithfully account for the energy received from transmission subsystems has been seriously undermined. This challenge is, however, being overcome by placement of shunt capacitors to supply the reactive power required for compensation;hence, optimal sitting and sizing of compensators has been intensively researched. As efficient as the use of meta-heuristic algorithms for joint optimal placement and sizing of the shunt capacitors are, employment of the approach on the Nigerian radial distribution system (RDS) is not yet popular as most of the earlier works reported rather used analytical and numerical programming approaches. In this study, therefore, the use of firefly algorithm (FA) on a Nigerian 11-kV feeder is presented as an approach to optimally site and size shunt capacitor for real power loss reduction on such network. Backward-forward sweep load flow technique, with voltage stability index (VSI), is employed to find the candidate buses where the shunt capacitors would be installed, then FA is employed to determine the optimal size required. This approach is implemented on a 34-bus 11-kV feeder and it is found out that the system?s real power loss reduced from 762.6419 to 597.7486?kW, while the minimum bus voltage magnitude was raised from 0.8295 to 0.8456 p.u. and the minimum system VSI was improved from 0.4741 to 0.5121 p.u. Based on these results, the proposed approach is, therefore, considered a promising technique for sitting and sizing shunt capacitor optimally in real practical RDS.

关键词： Backward-forward sweep firefly algorithm loss minimization voltage profile voltage stability index

来源：评论

学校读者我要写书评

暂无评论

Quantum-inspired firefly algorithm with ant miner plus for fake news detection

引用

INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第1期36卷

作者： Sharma, Kanta Prasad Manideep, A. Sai Kulkarni, Shailesh Gowrishankar, J. Choudhary, Binod Kumar Kaur, Jatinder Gehlot, Anita GLA Univ Dept Comp Engn & Applicat Mathura 281406 Uttar Pradesh India Vignans Fdn Sci Technol & Res Dept Management Studies Vadlamudi Andhra Pradesh India Vishwakarma Inst Informat Technol Dept Elect & Telecommun Engn Pune India JAIN Deemed Univ Sch Engn & Technol Dept Comp Sci Engn Bangalore Karnataka India ARKA Jain Univ Dept Elect & Elect Engn Ranchi Jharkhand India Vivekananda Global Univ Dept Elect Engn Jaipur 303012 Rajasthan India Uttaranchal Univ Uttaranchal Inst Technol Dept Elect & Commun Engn Dehra Dun 248007 India

Nowadays, technology has shifted the way individuals access news from conventional media sources to social media platforms. The active engagement of people with social media platforms leads them to consume news without confirming its source or legitimacy. This facilitated the dissemination of more manipulated and false information in the form of rumors and fake news. Fake news can affect public opinion and create chaos and panic among the population. Thus, it is essential to employ an advanced methodology to identify fake news with high precision. This research work has proposed the concept of the quantum-inspired firefly algorithm with the ant miner plus algorithm (QFAMP) for more effective fake news detection. The proposed QFAMP algorithm utilizes the attributes of quantum computing (QC), the firefly algorithm (FA), and the ant miner plus algorithm (AMP). Here, the QFA approach ensures the effective exploitation of the firefly agents until the agents are able to search for the brighter firefly. Further, the AMP algorithm utilizes the best ants with higher pheromone concentrations for global exploration, which also avoids the premature convergence of the QFA agents. In addition, the AMP algorithm serves as an efficient data mining variant that is effective for the classification of fake news. The efficacy of the proposed QFAMP algorithm is evaluated for the dataset of FakeNewsNet, which is composed of two sub-categories: BuzzFeed and PolitiFact. The experimental evaluations indicate the effective performance of the proposed algorithm compared to the other techniques.

关键词： Quantum computing metaheuristic algorithms firefly algorithm quantum firefly algorithm ant colony optimization ant miner plus fake news detection optimization

来源：评论

学校读者我要写书评

暂无评论

Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm

引用

RESULTS IN ENGINEERING 2024年 21卷

作者： Anshory, Izza Jamaaluddin, Jamaaluddin Wisaksono, Arief Sulistiyowati, Indah Hindarto Rintyarna, Bagus Setya Fudholi, Ahmad Rahman, Yuli Asmi Sopian, Kamaruzzaman Univ Muhammadiyah Sidoarjo Fac Sci & Technol Dept Elect Engn Sidoarjo Indonesia Natl Res & Innovat Agcy BRIN Res Ctr Energy Convers & Conservat Jakarta Indonesia Univ Muhammadiyah Sidoarjo Fac Sci & Technol Dept Informat Engn Sidoarjo Indonesia Univ Muhammadiyah Jember Dept Informat Engn Jember Regency Indonesia Univ Kebangsaan Malaysia Solar Energy Res Inst Bangi Selangor Malaysia Univ Tadulako Palu Indonesia Univ Teknol PETRONAS Dept Mech Engn Seri Iskandar 32610 Perak Darul Rid Malaysia

The use of solar photovoltaic panels as source of power for Brushless Direct Current (BLDC) motors requires a DCDC Converter circuit. One application of solar energy is as a power source for Brushless Direct Current (BLDC) motors. The main problem is the voltage fluctuation and low DC voltage generated by the solar panel. This research aims to improve the performance of the DC-DC Boost Converter circuit and minimize voltage fluctuations. The methodology encompasses mathematical modeling of the circuit in the form of transfer functions and optimizing the DC-DC Boost Converter circuit using the Proportional Integral Derivative (PID) controller and the firefly algorithm. Simulation testing results indicate an improvement in transient response performance of the DC-DC Converter circuit as a driver for the BLDC motor. This is evidenced by an increase in rise time from 499 s to 820 s, a decrease in settling time from 3.33 e+03 s to 2.07e+03 s, and a reduction in overshoot to 0 % from previously 11.4 %. The utilization of the firefly algorithm in optimization significantly enhances system efficiency, as demonstrated by faster achievement of stability without excessive oscillation and a reduction in the time required for the system to settle. Overall, this study shows that the firefly algorithm is effective in developing DC-DC Boost Converter circuits, improving system efficiency by reducing settling time and eliminating overshoot. These findings provide empirical evidence of the effectiveness of using artificial intelligence algorithms in enhancing the operational efficiency of energy conversion systems.

关键词： BLDC motor Solar panel DC-DC boost converter PID controller firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

引用

SOFT COMPUTING 2023年第6期27卷 2943-2964页

作者： Chen, Min-Rong Yang, Liu-Qing Zeng, Guo-Qiang Lu, Kang-Di Huang, Yi-Yuan South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Guangzhou Coll Commerce Sch Informat Technol & Engn Guangzhou 511363 Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Peoples R China

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

关键词： firefly algorithm extremal optimization Probability choice strategy Adaptive step size Continuous optimization problems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：