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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Sarangi, Shubhendu Kumar Panda, Rutuparna Priyadarshini, Sabnam Sarangi, Archana SOA Univ Dept Elect & Instrumentat Engn ITER Bhubaneswar Orissa India VSSUT Dept Elect & Telecommun Engn Burla India SOA Univ Dept Elect & Commun Engn ITER Bhubaneswar Orissa India

ISBN: (纸本)9781467399395

This paper intends to provide a modified firefly algorithm based on firefly algorithm and improved particle swarm optimization. This firefly algorithm is a category of nature-enthused algorithm of swarm intelligence, i. e. depends on the response of a firefly to the light of other fireflies and also perform well on various numerical optimization problems. The modified algorithm uses the improved velocity concept of particle swarm optimization to enhance the searching behavior of standard algorithm. A comparison of the firefly algorithm with that of modified firefly algorithm is performed for some standard benchmark functions through simulations. The algorithms are also checked in various standard dimensions for providing effective output. The simulated results prove the superiority of modified firefly algorithm as compared to the traditional firefly algorithm in standard benchmark functions and in all dimensions. The results give an idea that the proposed modified algorithm enriches performance of the standard firefly algorithm and converges more quickly with less time to produce optimum solution.

关键词： Particle swarm optimization firefly algorithm modified firefly algorithm Benchmark functions Dimensions

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Probabilistic Structural Model Updating with Modal Flexibility Using a modified firefly algorithm

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MATERIALS 2022年第23期15卷 8630-8630页

作者： Feng, Zhouquan Wang, Wenzan Zhang, Jiren Hunan Univ Coll Civil Engn Key Lab Wind & Bridge Engn Hunan Prov Changsha 410082 Peoples R China Hunan Univ Chongqing Res Inst Chongqing 401151 Peoples R China

Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified heuristic optimization algorithm named modified Nelder-Mead firefly algorithm (m-NMFA) was proposed to find the most probable values (MPV) of model parameters for the maximum a posteriori probability (MAP) estimate. The proposed m-NMFA was compared to the original firefly algorithm (FA), the genetic algorithm (GA), and the particle swarm algorithm (PSO) through the numerical illustrative examples of 18 benchmark functions and a twelve-story shear frame model. Then, a six-story shear frame model test was performed to identify the inter-story stiffness of the structure in the original and the damage states, respectively. By comparing the two, the position and extent of damage were accurately found and quantified in a probabilistic manner. In terms of optimization, the proposed m-NMFA was powerful to find the MPVs much faster and more accurately. In the incomplete measurement case, only the m-NMFA achieved target damage identification results. The proposed Bayesian model updating method has the advantages of high precision, fast convergence, and strong robustness in MPV finding and the ability of parameter uncertainty quantification.

关键词： structural health monitoring structural model updating modified firefly algorithm modal flexibility damage detection

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Parameter extraction for small-signal model of GaN HEMTs on SiC substrates based on modified firefly algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2022年第12期32卷 e23518-e23518页

作者： Liu, Yuan Cao, Fengrui Xiong, Xiaoming Huang, Junkai Deng, Wanling Guangdong Univ Technol Sch Integrated Circuits Guangzhou Peoples R China Jinan Univ Guangdong Higher Educ Inst Dept Elect Engn Key Lab New Semicond & Devices Guangzhou 510632 Peoples R China

In this article, an improved firefly algorithm is proposed to extract the parameters of the small signal model of GaN HEMTs on SiC substrates. First, the initial values of the parameters are extracted directly by pinch-off, un-biased cold S-parameters and hot S-parameters. Second, an improved firefly algorithm is proposed to optimize parameters. Finally, in order to verify this method, the performances of traditional algorithm are compared with modified firefly algorithm in convergence speed, execution time, and accuracy, which shows that this proposed method is more suitable for the extraction of high-dimensional parameters model.

关键词： GaN HEMTs on SiC substrates modified firefly algorithm small signal model

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Electronic Load Controller based on modified firefly algorithm to Reduce Frequency Fluctuation of Generator in Micro Hydro Power Plants

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2023年第2期13卷 601-611页

作者： Iksan, Nur Firdaus, Muhammad Ubaid Subiyanto Apriaskar, Esa Nugroho, Anan Udayanti, Erika Devi Widodo, Djoko Adi Semarang State Univ Fac Engn Dept Elect Engn Semarang Indonesia Dian Nuswantoro Univ Fac Comp Sci Dept Informat Syst Semarang Indonesia State Univ Semarang Fac Engn Dept Elect Engn Bldg E11 Semarang City 50229 Central Java Pr Indonesia

An overpowering condition generated by the PLMTH generator simulator will cause frequency instability which can result in some damage to electronic goods. So, it is necessary to design an optimal control system. One of the control systems used is the Electronic Load Controller (ELC) which can adjust the excess power to consumer loads and ballast loads. Development of an ELC that uses an electrical switch in the form of a Triac. This component will be controlled by a microcontroller with input data in the form of voltage, current, and frequency values. In the processing of microcontroller data, algorithms can be applied to obtain more optimal ELC performance. One of them is using the modified firefly algorithm (MFA). With this method, a PID tuning approach is taken so that it will get PID parameters on the Electronic Load Controller (ELC) system. In this MFA tuning process, up to 100 iterations are repeated to achieve optimal or near-optimal PID parameters. The results of this study were to measure the performance of the MFA ELC which was tested with a PLTMH generator simulator. Based on the test results, shows that the performance of the MFA ELC can reduce frequency fluctuations so that the frequency value is more stable within the standard limits set by the Ministry of Energy and Mineral Resources of the Republic of Indonesia of 50.1Hz - 51.5 Hz. In addition, the generated voltage also becomes more stable, namely in the range of 222.2 - 223.6 Volts. These results when compared with testing the generator without using the MFA ELC are very different, because the frequency values produced are in the interval 46.7 - 51.5 Hz and the voltage values are in the interval 214.2 - 244.5 Volts. Thus, the performance of the MFA ELC is sufficient to help reduce frequency instability in the PLTMH generator simulator.

关键词： Electronic Load Controller (ELC) Frequency Fluctuation modified firefly algorithm PID Controller

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Investigation on load harmonic reduction through solar-power utilization in intermittent SSFI using particle swarm, genetic, and modified firefly optimization algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 4117-4133页

作者： Albert, Johny Renoald Sharma, Aditi Rajani, B. Mishra, Ashish Saxena, Ankur Nandagopal, C. Mewada, Shivlal Vivekanandha Coll Engn Women Dept EEE Namakkal Tamil Nadu India ITM Vocat Univ Dept CSE Vadodara India Aditya Coll Engn & Technol Dept EEE Surampalem Andhra Pradesh India Gyan Ganga Inst Technol & Sci Dept CSE Jabalpur Madhya Pradesh India Indus Univ Indus Inst Informat & Commun Technol Dept CSE Ahmadabad Gujarat India M Kumarasamy Coll Engn Autonomous Dept ECE Karur Tamil Nadu India Goverment Holkar Sci Coll Dept Comp Sci Indore India

A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), modified firefly algorithm (MFA). It's determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model.

关键词： Symmetric solar fed inverter particle swarm optimization genetic algorithm modified firefly algorithm power quality

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MFO-TL: modified firefly optimal transfer learning based motion correction of fetal brain and placenta MRI for thyroid prediction

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EVOLVING SYSTEMS 2024年第2期15卷 361-374页

作者： Keerthi, G. Abirami, M. S. SRM Inst Sci & Technol Dept Computat Intelligence Chennai India

Thyroid classification is required in the medical domain to better assist doctors in deciding diagnostic treatments. Although many researchers conducted experiments to detect abnormal conditions of fetal brains in an earlier stage, there exist several limitations like over-fitting problems and imbalance data problems. To deal with these problems, this paper proposes 'Transfer learning- Bidirectional Long Short Term Memory (TL-BiLSTM) which is an efficient thyroid classification model. This paper focuses on identifying the defects in fetal brains in a primary stage by investigating the thyroid range of the mother during the 19th week of pregnancy. In this research, TL is applied with Bi-LSTM for the improvement of Thyroid classification performance. The Transfer learning method selects the optimal batch size for the Bi-LSTM model to eliminate the overfitting problem. The bi-LSTM model learns the sequence in forward and reverses mode to store the useful features for the long term and discard the irrelevant features. The most significant features in the dataset are selected by applying a modified firefly algorithm (MFA). The modified firefly algorithm has the advantages of easy escape from local optima and a good convergence rate. For evaluation purposes, the thyroid dataset is used as input for investigating the proposed classifier's effectiveness. The evaluation results display that the proposed novel approach successfully identifies and classifies thyroid problems using fetal brain magnetic resonance imaging (MRI) images of various Gestational weeks.

关键词： Bidirectional long short-term memory modified firefly algorithm Transfer learning Fetal brain defects Thyroid MRI images

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Double-layer firefly algorithm for Simultaneous Optimal Sizing and Operation of Energy Hubs

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第16期45卷 1846-1857页

作者： El-Zonkoly, Amany M. Abdelaziz, Almoataz Y. Eladl, Alaa M. AAST Coll Engn & Technol Dept Elect & Control Engn Alexandria Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt

For economical and efficient use of local multi-carrier energy systems, energy hubs (EHs) were introduced as the best solution. EHs as one type of distributed generation have made the use of multi-carrier energy systems attractive with better energy conservation. To achieve the economic and environmental objectives of the EH, the sizes of its components need to be optimally selected. In addition, to check the incentive of the EH, it must be studied under operation. For this reason, the economic dispatch of the EH is to be carried out. However, the two objectives of optimal design and optimal dispatch have been previously addressed as two separate problems one step after another. In this paper, a double-layer firefly based optimization algorithm is proposed to both optimally size and dispatch the EH components, simultaneously. The two problems are combined in one multi-objective optimization problem. The objectives are to minimize the total capital and operational cost of the EH, minimize the system emission, and maximize the cost of exported energy to the network. The proposed algorithm is effectively applied to a commercial facility (hospital) as a case study.

关键词： energy hubs optimal sizing optimal dispatch modified firefly algorithm

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Enhancing grid resilience and efficiency with distributed generators through fuzzified firefly optimization

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ELECTRICAL ENGINEERING 2024年第6期106卷 7803-7818页

作者： Anbuchandran, S. Christa, S. T. Jaya Suresh, S. Bhuvanesh, A. JCT Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India

In a conventional power system, distribution systems tend to incur higher power losses. However, integrating decentralized sources into power networks not only reduces these losses but also alleviates the strain on primary sources. Since centralized power sources are often situated at a distance, incorporating distributed generations (DGs) closer to load centers offers more efficient system operation. However, the lack of proper sizing alignment among secondary sources could result in operational, financial, and ecological complexities. Selecting optimal DG locations and sizes necessitates intricate computational processes, escalating workload demands. Fortunately, advancements in optimization software have simplified this task. The primary focus of this paper lies in optimizing DG allocation through the application of a fuzzified firefly algorithm (FFA). The strategic placement of diverse DG types aims to reduce both real and reactive power losses within the distribution system, followed by an evaluation of system performance indices. The study examines how different types of DGs incorporated into an IEEE-33 bus system elucidate the impact of varying power system loads, spanning from 50 to 150% of their rated value.

关键词： Curve fitting technique Distributed generators IEEE-33 bus modified firefly algorithm Pollutant emission Power loss minimization

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Energy-efficient virtual machine placement using enhanced firefly algorithm

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MULTIAGENT AND GRID SYSTEMS 2016年第3期12卷 167-198页

作者： Barlaskar, Esha Singh, Yumnam Jayanta Issac, Biju Assam Don Bosco Univ Sch Technol Dept Comp Sci & Engn & Informat Technol Gauhati Assam India Univ Teesside Sch Comp Middlesbrough Cleveland England

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing.

关键词： Energy efficiency virtual machine placement hierarchical clustering modified firefly algorithm

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A novel evolving framework for energy management in wind-gas reinforced unit commitment with demand response

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IET RENEWABLE POWER GENERATION 2022年第16期16卷 3473-3484页

作者： Safipour, Hamed Abdollahi, Amir Hajmohammadi, Mohammad Sadegh Alizadeh, Mohammad Iman Islamic Azad Univ Dept Elect Engn Sirjan Branch Sirjan Iran Shahid Bahonar Univ Dept Elect Engn Kerman Iran Islamic Azad Univ Sirjan Branch Dept Comp Engn Sirjan Iran Tarbiat Modares Univ Elect & Comp Engn Fac Tehran Iran

This research work tries to investigate the provision of flexibility on both sides of production and demand security constraint unit commitment (SCUC) before the schedule is considered. To this end, a powerful heuristic technique based on modified firefly algorithm (FA) is devised to achieve the optimal solution. Moreover, fast-starting gas units (FGUs) are considered as providers of lateral flexibility as a result of their quickness in starting work and high ramp capability. Considering the fact that the normal operation of units is subject to the supply of reliable gas through connected gas transmission lines, the uncertainty of gas supply in the suggested model is considered to imitate the effects of deviation in the volume of gas over pipelines. The application of the suggested model has been tested on IEEE-118 bus.

关键词： energy management powerful heuristic technique gas supply wind-gas reinforced unit commitment power generation dispatch power generation economics lateral flexibility demand response gas units high ramp capability modified firefly algorithm optimal solution reliable gas demand side management demand security constraint unit commitment novel evolving framework power generation scheduling pipelines power system security connected gas transmission lines

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