检索结果-内蒙古大学图书馆

An optimal frequency regulation in interconnected power system through differential evolution and firefly algorithm

SOFT COMPUTING 2024年第1期28卷 593-606页

作者： Mishra, Dillip K. Mohanty, Asit Ray, Prakash K. Clemson Univ Dept Automot Engn Clemson SC 29607 USA Eastern Acad Sci & Technol Dept Elect Engn Phulnakhara 754001 Odisha India Odisha Univ Technol & Res Dept Elect Engn Bhubaneswar 751003 Odisha India

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller.

关键词： Automatic generation control Differential evolution firefly algorithm Frequency control PID controller OPAL-RT

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Routing in Cluster Based Heterogeneous Wireless Sensor Network Using Hybrid GWO and firefly algorithm

引用

WIRELESS PERSONAL COMMUNICATIONS 2024年第2期137卷 997-1028页

作者： Dev, Jayashree Mishra, Jibitesh Odisha Univ Technol & Res Bhubaneswar Odisha India

Object tracking application is one of the important as well as challenging application of energy constrained Wireless Sensor Network (WSN).Timely and accurate action is required whenever the presence of object is sensed in the area of interest (AOI).This requires a lot of data collection from surrounding environment, routing of these data to base station (BS) and data processing. Large volume of data processing challenges the long survival of the WSN. In other way, expecting accurate tracking result with less data is quite impossible. Early exhaustion of node energy leads to early node death which affects the tracking quality. Therefore, it is required to maintain the accuracy in tracking result while not affecting the longevity of the network. Use of heterogeneous sensor nodes and cluster architecture of the network are two of the many factors that can enhance the lifetime of the network to some extent. Lifetime of the network can be further enhanced using optimum data routing procedure. This paper proposes a novel swarm intelligence based routing algorithm called HGWO-firefly algorithm for routing of information to BS in a WSN with cluster architecture. Proposed algorithm consists of two broad steps. In first step, potential cluster head (CH) selection is done using hybrid K-means and Grey Wolf Optimization (GWO) algorithm. In second step, selection of energy efficient route in between BS and sensor node is done using firefly algorithm. When the performance of the proposed algorithm is compared with existing PSO-based routing algorithm and FIGWO algorithm, it is found that the performance of the proposed algorithm is better.

关键词： Wireless sensor network K-Means algorithm Grey wolf optimization, Particle swarm optimization Hybrid grey wolf optimization firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing mechanical and tribological performance of hybrid composites: An experimental study utilizing response surface methodology and firefly algorithm

引用

POLYMER COMPOSITES 2024年第17期45卷 15924-15940页

作者： Dash, Soumya Satpathy, Mantra Prasad Routara, Bharat Chandra Pati, Pravat Ranjan Gantayat, Subhra KIIT Univ Sch Mech Engn Bhubaneswar 751024 India CV Raman Global Univ Dept Mech Engn Bhubaneswar India Graphic Era Univ Dept Mech Engn Dehra Dun India KISS Univ Sch Indigenous Knowledge Sci & Technol Bhubaneswar India

This study emphasizes the significance of stacking sequence and hybridization of glass, carbon, kevlar and basalt fibers to enhance the mechanical characteristics and the overall wear response of polymer composites. The carbon layer on the outside of the composite exhibited higher ultimate tensile and flexural strengths. The abrasive wear of fabricated hybrid composites is also explored by performing experiments using Box-Behnken design approach. The pin-on-disc tester is utilized to do the wear test by varying composite type, sliding distance, and sliding velocity, with specific wear rate (SWR) serving as the response parameter. Regression analysis is performed to predict SWR using control and response parameters derived from experimentation. A novel firefly algorithm technique is adopted to determine the optimal process parameter combination. By utilizing optimized parameters (430 m, 10.5 m/s, and the CKBG4BKC stacking sequence), the SWR is considerably reduced to 16.82 x 10-5 mm3/Nm. Scanning electron microscopy on the worn-out wear surface reveals enhanced interfacial bonding, fiber breakage and plowing as the fundamental wear mechanism. This work provides insight into hybrid composites for constructing aircraft and automobile body structures, where they provide an optimal blend of strength, sustainability, and structural *** Hybrid composite: Stacking sequence impacts on mechanical and abrasive wear. Box-Behnken design: Applied on stacking order, sliding distance and velocity. Utilizing metaheuristic firefly algorithm to enhance specific wear results. Optimal parameters: 430 m, 10.5 m/s, and CKBG4BKC stacking sequence. Lightweight, high-strength, cost-effective, and sustainable hybrid composites. Tribological Performance of Hybrid Composite. image

关键词： box-Behnken design firefly algorithm hybridization microstructure stacking sequence

来源：评论

学校读者我要写书评

暂无评论

Construction of Digital Image Segmentation Automation Processing System Based on Improved firefly algorithm

引用

IEEE ACCESS 2024年 12卷 177189-177203页

作者： Zhou, Youli Zhang, Chao Yiyang Vocat & Tech Coll Sch Elect & Informat Engn Yiyang 413049 Peoples R China Hunan Int Econ Univ Sch Informat & Mech & Elect Engn Changsha 410205 Peoples R China

With the rapid development of information technology and digital technology, the generation of massive image data has put forward higher requirements for image processing technology. Image segmentation, as an important step in image processing, also faces huge challenges. Therefore, this study improved the firefly algorithm by integrating adaptive step size, covariance elite selection, and neighborhood search scheme, and constructed a grayscale threshold image segmentation model based on the improved algorithm. The test results showed that the Jacobian values of the proposed model at thresholds of 2, 3, 4, and 5 were 0.907, 0.919, 0.946, and 0.957, respectively, and the Dice coefficients were 0.9187, 0.951, 0.9617, and 0.9586, respectively. After image segmentation, the optimal peak signal-to-noise ratio and structural similarity index were 22.8462 and 0.76281, respectively. The experimental results show that the research can effectively improve the accuracy and edge preservation ability of image segmentation by combining the improved swarm intelligence algorithm with grayscale threshold segmentation technology, providing new technical means and solutions for the field of image segmentation, and has certain practical application value.

关键词： firefly algorithm image segmentation adaptive adjustment grayscale threshold optimization solution firefly algorithm image segmentation adaptive adjustment grayscale threshold optimization solution

来源：评论

学校读者我要写书评

暂无评论

UV-Vis spectroscopy coupled with firefly algorithm-enhanced artificial neural networks for the determination of propranolol, rosuvastatin, and valsartan in ternary mixtures

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Serag, Ahmed Abduljabbar, Maram H. Althobaiti, Yusuf S. Almutairi, Farooq M. Alsharif, Shaker T. Alzhrani, Rami M. Ahmed, Marwa F. Almalki, Atiah H. Al Azhar Univ Fac Pharm Pharmaceut Analyt Chem Dept Nasr City Cairo 11751 Egypt Taif Univ Coll Pharm Dept Pharmacol & Toxicol POB 11099 Taif 21944 Saudi Arabia Taif Univ Addict & Neurosci Res Unit Hlth Sci CampusPOB 11099 Taif 21944 Saudi Arabia Univ Hafr Albatin Coll Appl Med Sci Dept Clin Labs Sci Hafr Albatin 39524 Saudi Arabia Umm Al Qura Univ Coll Pharm Dept Pharmaceut Sci Mecca Saudi Arabia Taif Univ Coll Pharm Dept Pharmaceut & Ind Pharm POB 11099 Taif 21944 Saudi Arabia Taif Univ Coll Pharm Dept Pharmaceut Chem POB 11099 Taif 21944 Saudi Arabia

In the present study, a simple, rapid and cost-effective analytical method was developed for the simultaneous determination of three commonly prescribed cardiovascular drugs: propranolol, rosuvastatin and valsartan. The method employed artificial neural networks (ANN) to model the relation between the UV absorption spectra of the drugs and their concentrations. An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. Validation of the developed FA-ANN models demonstrated excellent accuracy, precision and selectivity for the quantification of the target analytes as per international conference on harmonisation (ICH) guidelines. Additionally, the greenness, analytical practicality and sustainability of the developed models were assessed using the analytical greenness (AGREE), blue applicability grade index (BAGI) and the red-green-blue (RGB) tools, confirming their environmentally friendly, practical and sustainable nature. This research shed the light on the potential of ANN coupled with UV fingerprinting for the rapid and simultaneous determination of critical cardiovascular drugs posing a significant impact on pharmaceutical quality control and patient monitoring.

关键词： Artificial neural networks Cardiovascular diseases firefly algorithm Greenness assessment Spectrophotometry

来源：评论

学校读者我要写书评

暂无评论

A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants

引用

WATER SCIENCE AND TECHNOLOGY 2024年第10期90卷 2747-2763页

作者： Achite, Mohammed Samadianfard, Saeed Elshaboury, Nehal Toubal, Kamel Abderezak Abdelkader, Eslam Mohammed Sharafi, Milad Hassiba Benbouali Univ Chlef Fac Nat & Life Sci Water & Environm Lab Chlef 02180 Algeria Univ Tabriz Dept Water Engn Fac Agr Tabriz *** Iran Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Giza 12311 Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt Urmia Univ Fac Agr Dept Water Engn Orumiyeh Iran

Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH3 concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.

关键词： Algeria energy consumption modeling firefly algorithm support vector regression wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

A new evolutionary optimization based on multi-objective firefly algorithm for mining numerical association rules

引用

SOFT COMPUTING 2024年第9-10期28卷 6879页

作者： Rokh, Babak Mirvaziri, Hamid Olyaee, Mohammadhossein Univ Zanjan Dept Comp Engn Zanjan *** Iran Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran Univ Gonabad Fac Engn Dept Comp Engn Gonabad Iran

Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of interest in numerical association rules mining (NARM) is controlling the length of itemset intervals. In this paper, we propose a novel evolutionary algorithm based on the multi-objective firefly algorithm for efficiently mining numerical association rules (MOFNAR). MOFNAR utilizes Balance, square of cosine (SOC) and comprehensibility as objectives of evolutionary algorithm to assess rules and achieve a rule set that is both simple and accurate. We introduce the Balance measure to effectively control the intervals of numerical itemsets and eliminate misleading rules. Furthermore, we suggest a penalty approach, and the crowding-distance method is employed to maintain high diversity. Experimental results on five well-known datasets show the effectiveness of our method in discovering a simple rule set with high confidence that covers a significant percentage of the data.

关键词： Data mining Numerical association rule mining Multi-objective evolutionary algorithm firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

A Global Best-guided firefly algorithm for Engineering Problems

引用

Journal of Bionic Engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The firefly algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization Engineering design

来源：评论

学校读者我要写书评

暂无评论

Optimizing Social Media Marketing Strategies Through Sentiment Analysis and firefly algorithm Techniques

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 1131-1140页

作者： Anakal, Sudhir Lakshmi, P. N. S. Fofaria, Nishant Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Sanjeera, Shaik El-Ebiary, Yousef A. Baker Patel, Ritesh Sharnbasva Univ Dept Master Comp Applicat Kalaburagi India Aditya Univ Dept CSE Surampalem Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India UniSZA Univ Fac Informat & Comp Kuala Terengganu Malaysia Savitribai Phule Pune Univ Sanjivani Coll Engn Pune India

The dramatic expansion of social media platforms reshaped business-to-customer interactions so organizations need to refine their marketing strategies toward maximizing both user engagement and marketing return on investment (ROI). Presentday social media marketing methods struggle to embrace user emotions fully while responding to market variations thus demonstrating the necessity for developing innovative social media marketing tools. Studies seek to boost social media marketing performance through an FA integration with sentiment analysis for content strategy optimization and better user engagement results. This study adopts novel techniques by combining sentiment analysis with the firefly algorithm to optimize marketing strategies in real-time and it represents an underutilized approach in present research. Eventually combined fields generate a sentiment-driven and data-oriented decision- making capability in social media marketing applications. The proposed system combines sentiment analysis technology that measures social media emotion levels alongside the firefly algorithm which applies optimization methods to marketing tactics based on present feedback. The framework operates through dynamic adjustments of content strategies which maximize user engagement. The proposed method demonstrated 98.4% precision in forecasting user engagement metrics and adapting content strategies. Results show traditional marketing strategies yield to these approaches by improving user interaction alongside campaign effectiveness. The research introduces a new optimization method in social media marketing which integrates sentiment analysis with firefly algorithm technology. Research findings suggest this combined methodology brings substantial precision improvements to marketing strategies by offering companies an effective method to optimize digital marketplace outcomes.

关键词： Sentiment analysis firefly algorithm social media marketing optimization user engagement marketing strategies

来源：评论

学校读者我要写书评

暂无评论

Hybrid multi-objective particle swarm optimization feature selection approach with firefly algorithm using decision tree classifier

引用

EVOLUTIONARY INTELLIGENCE 2025年第2期18卷

作者： Singh, Ashish Kumar Kumar, Anoj MNNIT Allahabad Dept Comp Sci & Engn Prayagraj 211004 Uttar Pradesh India

In machine learning, the importance of relevant data increases exponentially. In our proposed approach, we introduce an optimization method that combines Particle Swarm Optimization (PSO) and the firefly algorithm (FA) to enhance feature selection using decision tree-based classification. PSO is well-suited for small search spaces, while the firefly algorithm is effective for large search spaces. The proposed method, PSOFA-DT aims to improve classification performance by reducing dimensionality and optimizing feature selection. PSO's global search capabilities are complemented by FA's localized search, and the algorithm's effectiveness is evaluated using decision tree accuracy and hold-out cross-validation. Experimental results demonstrate that PSOFA-DT outperforms individual implementations of PSO and FA in feature reduction and classification accuracy. Decision tree accuracy is used as the primary fitness metric, while the firefly algorithm refines the feature selection process. The algorithm balances exploration and exploitation by adjusting key parameters such as inertia weight, learning factors, and attraction coefficients. The firefly algorithm further optimizes feature selection, enhancing decision tree performance.

关键词： PSO firefly algorithm Multi-objective Feature Decision tree

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：