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Optimal Power Allocation Based on OFDM-NOMA Network Using firefly optimization

ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第18期51卷 2116-2124页

作者： Khandelwal, Hardika Somkuwar, Ajay Yadav, J. S. Maulana Azad Natl Inst Technol Dept Elect & Commun Engn Bhopal Madhya Pradesh India

Non-Orthogonal Multiple Access (NOMA) is emerging as a capable aspirant for fifth-generation (5G) technology. High spectral efficiency and massive connectivity demands are satisfied by NOMA along with Orthogonal Frequency Division Multiplexing (OFDM). In this article, we describe the NOMA technique with the OFDM channel selection technique. The source signal is modulated by Quadrature Amplitude Modulation (QAM) and encoded by the fast Fourier Transform (FFT) encoder. The transmitted signal is transferred under the optimal power allocation using the firefly optimization algorithm in the OFDM NOMA technique. The fitness function is derived with power constraint and is optimized using the firefly optimization algorithm. The performance parameters of the suggested scheme are Signal to Noise Ratio (SNR), Bit Error Rate (BER), Spectrum Efficiency (SE), and Energy Efficiency (EE) which are computed and attain better outcomes when compared with the traditional OFDM technique along with Orthogonal Multiple Access technique (OMA).

关键词： NOMA OFDM optimal power allocation firefly optimization algorithm

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Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning

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MATERIALS & DESIGN 2024年 248卷

作者： Wu, Hao Zhang, Jianyuan Zhang, Jintao Ge, Chengjie Ren, Lu Suo, Xinkun Ningbo Univ Multidimens Addit Mfg Inst Fac Mech Engn & Mech Ningbo 315211 Peoples R China

Solid solution strengthening theory is essential for designing steel with high microhardness. Experimental determination is quite time consuming and costly. It is necessary to develop an alternate approach to rapidly and accurately predict new solid solution strengthening theory for steel. In this study, a data-driven model combining machine learning (ML), firefly optimization algorithm (FA) and conditional generative adversarial networks (CGANs) were proposed to predict solid solution strengthening theory of Fe-C-Cr-Mn-Si steel. Three alloys were fabricated using cladding to validate the predict accuracy of the models. The results show that the trained support vector regression (SVR) model demonstrated the highest prediction precision for microhardness. The coefficient of determination (R2) value increased from 0.85 to 0.89 and root mean square error (RMSE) decreased from 0.39 to 0.31 after introducing the modified solid solution strengthening theory. The experimental validation revealed a minimum error of 1.17% between the predicted value and the experimental value. The investigation provides a valuable method to expedite design of Fe-C-Cr-Mn-Si steel with extreme high accuracy.

关键词： Fe-C-Cr-Mn-Si steel Machine learning Conditional generative adversarial networks Solid solution strengthening firefly optimization algorithm

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Heat pipes and nanofluids utilization for cooling photovoltaic panels: an application of hybrid machine learning and optimization models

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1078-1088页

作者： Abdulrahman, Aysar A. Sulaimani Polytech Univ Coll Informat Dept Comp Networks Sulaymaniyah Iraq

This study explores the synergies between advanced cooling technologies and photovoltaic systems, seeking to improve their overall efficiency and contribute to the broader goal of mitigating greenhouse gas emissions. To cool photovoltaic panels in more efficiently maner, understanding heat pipes, nanofluids, and panels interaction play key roles. For analysis and optimization, hybrid models of convolutional neural network (CNN) and firefly optimization algorithm are employed. The firefly optimization algorithm is used to optimize the thermosiphon heat pipe's operational conditions, taking into account inputs such as the filling ratio, nanofluid concentration and panel angle. The study compared the predicted outcomes of a classic CNN model to laboratory experiments. While the CNN model was consistent with experimental findings, it struggled to predict high power values with precision. The proposed model improved high power value predictions by 4.05 W root mean square error (RMSE). The proposed model outperformed the classic CNN model for values greater than 50 W, with an RMSE of 3.95 W. The optimal values for the filling ratio, nanofluid concentration and panel angle were determined after optimization with the firefly algorithm. The research contributes to the advancement of renewable energy technologies and the optimization of photovoltaic panel cooling and energy production. Nanofluid with 1% mass concentration improves photovoltaic collector thermal efficiency due to its higher thermal conductivity coefficient. The photovoltaic collector's electrical efficiency peaks in the morning, drops at noon due to temperature and radiation and recovers by morning. Electrical efficiency is best with nanofluid at 0.86%. Exergy efficiency closely matches electrical efficiency, with nanofluid at the optimal percentage achieving the highest efficiency and water cooling the lowest.

关键词： photovoltaic panel nanofluids heat pipe convolutional neural network firefly optimization algorithm photovoltaic panel efficiency

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Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images

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IMAGING SCIENCE JOURNAL 2024年第8期72卷 1243-1262页

作者： Abirami, S. Lanitha, B. Karpagam Acad Higher Educ Dept Comp Sci & Engn Coimbatore 641021 Tamilnadu India

Brain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie firefly optimization algorithm (BBCFO). The BBCFO is the incorporation of Border Collie optimization (BCO), the firefly optimization algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.

关键词： Shepard convolutional neural network bald eagle search algorithm border collie optimization magnetic resonance imaging firefly optimization algorithm U-Net plus plus Shepard Convolutional Neural Network feature extraction

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An Intrusion Detection System Using the Artificial Neural Network-based Approach and firefly algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2024年第4期137卷 2409-2440页

作者： Rajabi, Samira Asgari, Samane Jamali, Shahram Fotohi, Reza Univ Mohaghegh Ardabili Comp Engn Dept Ardebil Iran Iran Univ Sci & Technol Ctr Excellence Future Networks Tehran Iran Shahid Beheshti Univ Fac Comp Sci & Engn Tehran *** Iran

Due to the dynamic nature and limited resources in wireless networks, attack occurrence is inevitable. These attacks can damage or weaken the transmitted packets and threaten the entire system's efficiency. As a result, in such a situation, great and sometimes irreparable damage will be done to the business. Thus, security and attack prevention in wireless networks become a necessity and are very important. Essence intrusion detection systems determine whether a user's performance and behavior under the control or activity of a network traffic load is malicious. Since the characteristics of user behavior and network traffic are diverse and numerous, Selecting some features is necessary to improve the classification accuracy. Therefore, in this idea, a new model for estimating the penetration of wireless network-based networks is proposed based on a combination of feature subset selection based on firewall algorithm and fast neural learning networks. In this paper, the proposed idea will use the training set from the data set collected to test intrusion detection systems called KDD Cup to determine network intrusion detection methods and evaluate the proposed model. The proposed idea, based on the results obtained from the simulation and its performance in various experiments, has shown that it has improved significantly in terms of multiple criteria such as accuracy, F-criterion rate, and efficiency compared to the neural network pattern. In other words, the proposed idea performs better than the neural network method in identifying healthy nodes and new malicious intrusions in the target network. The simulation outputs also indicate that the proposed idea has a better classification rate and F-criteria than the FLN methods based on HSO, ATLBO, GA, and PSO. Vector backup machine, multilayer perceptron network, DBN, and S-NDAE have less time.

关键词： Intrusion detection system (IDS) firefly optimization algorithm Fast learning neural network KDD cup dataset

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Enhancing workflow efficiency with a modified firefly algorithm for hybrid cloud edge environments

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Alsadie, Deafallah Alsulami, Musleh Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca 21961 Saudi Arabia Umm Al Qura Univ Coll Comp Dept Software Engn Mecca 21961 Saudi Arabia

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified firefly optimization algorithm (ModFOA) and comparing it with established methods such as Ant Colony optimization (ACO), Genetic algorithm (GA), and Particle Swarm optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA's potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA's parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.

关键词： Scientific workflows Cloud computing Scheduling algorithms firefly optimization algorithm Resource utilization

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RF Chain Selection Using Hybrid optimization with Precoding in mm-Wave Massive MIMO Systems

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期131卷 1997-2017页

作者： Srinivas, Ch V. V. S. Borugadda, Somasekhar Andhra Univ Coll Engn AUCE A ECE Dept Visakhapatnam 530003 Andhra Pradesh India Vishnu Inst Technol ECE Dept Bhimavaram 534202 Andhra Pradesh India Anil Neerukonda Inst Technol & Sci ECE Dept Visakhapatnam 531162 Andhra Pradesh India

Energy efficiency is considered the prominent metric in data communication systems and has a greater impact on designing Millimetre-wave (mm-wave) multiple input multiple output (MIMO) systems. As the recent mm-wave system relies upon the number of radiofrequency (RF) chains, effective RF chains are preferred to maximize overall system performance, energy and spectral efficiency. Degraded system efficiency and increased path loss are the major limitations faced in MIMO systems. This harms the data rate transmission between transmitter and receiver antennas. Hence, an effectual mm-wave with a massive MIMO model is presented in this research by adopting standard techniques. The chief objective of this research is to maximize energy and spectral efficiency with an appropriate selection of RF chains. The optimal RF chains are selected through a hybrid optimization model called the differential evolution firefly selection model (DEA + FOA). After selecting optimal RF chains, an appropriate precoding scheme is used to route the transmitted data over the preferred direction by overcoming the path-loss issues. In the precoding step, the successive interference cancellation based technique is employed to optimize the RF chain's sub-capacity and increase the system efficiency. The performance of a proposed model is analyzed by evaluating the energy and spectral efficiency by varying the number of RF chains at low and high signal-to-noise ratio rates. The proposed optimization and precoding techniques are compared with existing approaches to analyze the efficacy of a proposed MIMO system. The proposed model has proven extremely effective in transforming data from transmitter to receiver with increased system efficiency.

关键词： mm-wave communication RF chain selection Digital to analog converter firefly optimization algorithm SIC precoding Differential evolution algorithm Energy-spectral efficiency

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Two-loop controller design and implementations for an inverted pendulum system with optimal self-adaptive fuzzy-proportional-integral-derivative control

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2022年第2期44卷 468-483页

作者： Abut, Tayfun Soyguder, Servet Mus Alparslan Univ Dept Mech Engn TR-49100 Mus Turkey Firat Univ Dept Mech Engn Elazig Turkey Ankara Yildirim Beyazit Univ Dept Ind Engn Ankara Turkey

Since inverted pendulum systems (IPSs) are under-actuated systems, controlling these systems has become an important problem. Because of their irregular movements and being structurally unstable, the balancing of these systems poses a major problem. In this study, the dynamic model of a linear IPS has been obtained by using the Newton-Euler method and also conventional proportional-integral-derivative (PID), fuzzy logic (FLC), and self-adaptive fuzzy-proportional-integral-derivative (SAF-PID) control algorithms have been designed for the control of the system. The purpose of these designed controllers is to keep the arms of the IPS on the moving cart in a vertical position and bring the cart to the specified equilibrium position. In conventional control methods, unwanted oscillations have occurred during the movement of the cart. By using two-loop control methods, it is aimed to reduce these oscillations and to design a robust system. The results have been obtained using the conventional PID, FLC, and SAF-PID controllers. To increase the performance of the SAF-PID control type, the optimum value of the coefficients of the points where the legs of the membership functions touch were calculated using the firefly optimization algorithm. To conclude, the designed controllers were performed on computer simulation. The results are given graphically and numerically. Mean absolute error (MAE), mean squared error, integral absolute error IAE, and integral squared error have been compared and examined with each other and with the studies in the literature using performance criteria. As a result, the proposed control method was at least 26.31% more successful in the angular control of the pendulum and at least 9.26% better in the position control of the cart than the studies in the literature.

关键词： Optimal self-adaptive fuzzy-proportional-integral-derivative control firefly optimization algorithm inverted pendulum system under-actuated system two-loop control

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Predicting the compressive strength of self-compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network

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STRUCTURAL CONCRETE 2022年第2期23卷 1191-1213页

作者： Pazouki, Gholamreza Golafshani, Emadaldin Mohammadi Behnood, Ali Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture can provide a wide variety benefits such as improving the mechanical properties, reducing the construction costs, and enhancing the environmental conditions. Compressive strength as one of the most critical mechanical parameters used in many design codes can be obtained either through costly and time-consuming experiments or non-destructive methods such as predictive models. In this study, a hybrid model of the radial basis function neural network (RBFNN) and firefly optimization algorithm (FOA) (RBFNN + FOA) was proposed for the prediction of the compressive strength of self-compacting concrete (SCC) containing CFFA. For this purpose, a multi-laboratory dataset containing 327 SCC samples made with CFFA was employed. The input parameters in the proposed models included the age of the specimen and the amounts of cement, water, CFFA, coarse aggregate, fine aggregate, and superplasticizer, and the only output parameter was the compressive strength of SCC. For comparison purpose, the artificial neural network (ANN) was also used to model the compressive strength of SCC containing CFFA. The results show that the compressive strength estimated by the proposed and ANN models have good accuracies compared to the experimental results. Moreover, several performance metrics showed that the accuracy of the proposed model is higher than that of the model developed based on ANN algorithm.

关键词： Class F fly ash compressive strength firefly optimization algorithm radial basis function neural network self‐ compacting concrete

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Research on the Combination of firefly Intelligent algorithm and Asphalt Material Modulus Back Calculation

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MATERIALS 2022年第9期15卷 3361-3361页

作者： Zhao, Runmin Gong, Jinzhi Zheng, Yangzezhi Huang, Xiaoming Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Transportat Bur Jinhu Huaian 211600 Peoples R China Southeast Univ Natl Demonstrat Ctr Expt Educ Rd & Traff Engn Nanjing 211189 Peoples R China

The modulus of asphalt pavement material is a necessary parameter for the design, strength measuring and stability evaluation of asphalt pavement. To get more precise test data for asphalt pavement material modulus, a new modulus back calculation method is proposed in this article, named as the firefly Asphalt Back Calculation Method (FABCM). This novel method uses the firefly optimization algorithm, which is a kind of particle swarm intelligence algorithm imitating the information transfer process among fireflies. To demonstrate the reliability and stability of FABCM, and to study the feasibility of multi-parameter modulus back calculation methods, this article used theoretical deflection curves calculated by BISAR3.0 and the actual measurement data of deflection curves and vertical pressures on the subgrade top surfaces on the full-scale test circular track in the Research Institute of Highway, Ministry of Transport (RIOHTrack) to conduct a modulus back calculation. The results show that FABCM only takes 0.5-1 s for each calculation, and the back calculation errors in the verification of FABCM are mostly smaller than 1%, which means that the firefly optimization algorithm was modified effectively in this article. Moreover, this article also indicates some key factors influencing the accuracy of modulus back calculation, and several reasonable suggestions to the application of modulus back calculation.

关键词： back calculation of modulus Falling Weight Deflectometer (FWD) firefly optimization algorithm RIOHTrack

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