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2nd International Conference on Materials Manufacturing and Modelling, ICMMM 2019

作者： Narayana Reddy, B. Hema, P. Vishnu Vardhan, G. Padmanabhan, G. Department of Mechanical Engineering Sri Venkateswara University Tirupati517502 India

Laser Beam Welding (LBW) is extensively being utilized in manufacturing processes to join dissimilar metals and alloy steels because its special advantages of controlled heating, Low Heat Affected Zone (HAZ) and smaller weld bead formation. However, it is viewed as a strange strategy with applications normally and constrained to welding of thick plates of metals. With a new generation of high power lasers, there has been a renewed interest in thick section LBW (also known as Keyhole Laser Welding (KLW)). KLW makes the process a possibility for industrial applications dealing with thick metals for welding in power plants, pipelines, offshore structures, shipbuilding. The advantages provided by such LBW, is suitable for joining at high process speed, low heat input, and to achieve high productivity, leading to significantly reduction in process costs. LBW of dissimilar metals such as alloy steel and stainless steel is still a challenging task, particularly due to the formation of martensitic formation in HAZ, brittle phases in the weld and solidification cracking in the fusion zone. Such issues can significantly deteriorate strength of the welded joint. Therefore, the aim of the present work is to examine the basic phenomena that happen inside the different weld zones and their impact on weld quality in the wake of LBW joints. Taguchi L25 is chosen and experiments are conducted with LBW considering 2 mm thick dissimilar metals, i.e. AISI 4130 and AISI 309 stainless steel by varying Laser Power, Welding Speed, Beam Angle, Focal Point Position and Focal Length. The experimental output results that are measured for the mechanical properties like Ultimate Tensile Strength and Impact Strength. The Analysis of Variance (ANOVA) is carried out to obtain the influence of the process parameters and statistical evolution of the results. firefly optimization algorithm is used to determine the optimal combination of the process parameters of the LBW © 2019 Elsevier Ltd.

关键词： ANOVA firefly algorithm Heat affected zone (HAZ) Laser Beam Welding

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Fuzzy Logic with firefly Algortihm for Semi-active Suspension System using Magneto-rheological Damper

Fuzzy Logic with Firefly Algortihm for Semi-active Suspensio...

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IEEE Symposium on Industrial Electronics and Applications

作者： Mat Hussin Ab Talib Intan Zaurah Mat Darus Faculty of Mechanical Engineering Universiti Teknologi Malaysia Skudai Johor MY

Semi-active (SA) suspension system with mathematical model is established based on a quarter vehicles. Magneto-rheological (MR) damper is used to change a conventional damper system to be as an intelligent damper. It contains a particle magnetic polarizable and suspended into a liquid form. The Bouc-Wen model of MR damper is used to determine the required damping force based on force-displacement and force-velocity characteristics. The performance of Intelligent Fuzzy Logic (IFL) controller optimized by firefly algorithm (FA) is investigated to control MR damper system. During this research, the gain scaling of IFL will be optimized using FA technique in order to achieve the lowest Mean Square Error (MSE) of the system response. The performance of the proposed controller then will be compared with conventional modified skyhook controller and uncontrolled system in term of body displacement, body acceleration, suspension deflection and tire deflection. Two Bump disturbance signals are implemented into the system. The simulation results demonstrates that the FA tuned IFL exhibits an improvement to the ride comfort and has the smallest MSE as compared to the performance of modified skyhook and uncontrolled system.

关键词： Suspension system Semi-active Fuzzy logic Modified skyhook firefly algorithm

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Study on Transformer Fault Diagnosis Based on IAVOA 3

Study on Transformer Fault Diagnosis Based on IAVOA

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zuo, Huixiang Xv, Yaosong Liaoning Technical University Faculty of Electrical and Control Engineering Huludao China

ISBN: (纸本)9798350382426

Based on the low accuracy of transformer fault diagnosis and less complexity, a research algorithm based on IAVOA is proposed. Firstly, obtain relevant data through gas decomposition in oil (DGA) and form 24-dimensional features, integrate the core principal component analysis to solve the problem of the compressor. Secondly, improve the African Vulture optimization algorithm (IAVOA), improve its optimization ability, avoid the local optimization, to optimize the parameters and weights of the kernel function in the hybrid core limit learning machine (HKELM) to increase the classification accuracy and improve the generalization ability and accuracy. The test results showed that the IAVOA-HKELM model achieved 96% accuracy, which improved by 10% over the ABC-HKELM model, 8% and 7%, respectively. © 2023 IEEE.

关键词： African vulture optimization algorithm convex lens reverse learning Fault diagnosis firefly algorithm limit learning machine

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FFBAT: A security and cost-aware workflow scheduling approach combining firefly and bat algorithms

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第24期29卷 e4295.1-e4295.13页

作者： Arunarani, A. R. Manjula, D. Sugumaran, Vijayan Anna Univ India Dept Comp Sci & Engn Madras Tamil Nadu India Oakland Univ Dept Decis & Informat Sci Rochester MI 48063 USA

Cloud computing is distributed computing on a large scale driven by practical and effective operations, in which a pay-per-use framework provides dynamic scaling in response to the needs of workflow applications. Many existing cloud computing environments do not effectively employ security measures to counter security threats in task scheduling. To improve the scheduling system, we include security service to the scheduling process. However, adding security services to applications inevitably causes overhead in terms of computation time. The tradeoff between achieving high computing performance and providing the desired level of security protection imposes a big challenge for task scheduling. To solve this problem, we propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. Our proposed algorithm is based on the hybrid optimization approach, which combines firefly and Bat algorithms. The coding strategy is to minimize the total execution cost while meeting the deadline and risk rate constraints. The proposed system uses a multi-objective function, and the results indicate that our algorithm always outperforms the traditional algorithms.

关键词： Bat algorithm computational cost firefly algorithm risk rate security workflow scheduling

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A Novel Hybrid FA-Based LSSVR Learning Paradigm for Hydropower Consumption Forecasting

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Journal of Systems Engineering and Electronics 2015年第5期26卷 1080-1101页

作者： TANG Ling WANG Zishu LI Xinxie YU Lean ZHANG Guoxing School of Economics and Management Beijing University of Chemical Technology School of Management Lanzhou University

Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.

关键词： Artificial intelligence firefly algorithm hybrid model hydropower consumption leastsquares support vector regression time series forecasting.

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Comparative Study of Optimal Location of Electrical Vehicle Charging Stations Using Different algorithms 1

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2nd International Conference on Smart Grid Energy Systems and Control, SGESC 2023

作者： Dwivedi, Avneesh Kakran, Sandeep National Institute of Technology Kurukshetra India

ISBN: (数字)9789819759514

ISBN: (纸本)9789819759507

Currently, pollution in the world is increasing day by day, and one of the major contributing factors is CO2 emission. Road transport in India caters to 87–90% of total passenger traffic and 64% of freight movement, which is the world's second-largest road network. As conventional fuel-based transport has the highest contribution to carbon emission, this provides a great opportunity to decarbonization of the transport sector and provides sustainable mobility. Electric vehicles can help to achieve sustainable mobility. A big infrastructure of electric vehicle charging stations is needed for the continuous mobility of electric vehicles. The Indian government supports cutting-edge technologies of electric vehicle development and its charging infrastructure. Charging infrastructure consists of various charging stations and charging points. These stations have chargers that are either installation-based or plug and play-based. This paper proposes the optimal allocation of electrical vehicle charging stations on the IEEE 33-bus distribution system using different heuristic techniques. The objective is to optimally locate the electrical vehicle charging stations in the radial distribution grid, minimizing the losses and improving the voltage profile. A comparative study is done using genetic algorithm, gray wolf optimization technique and firefly algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Charging infrastructure Electric vehicle Electric vehicle charging station firefly algorithm Genetic algorithm Gray wolf optimization technique

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Predicting Genetic Disorders: A Link Mining Approach 2

Predicting Genetic Disorders: A Link Mining Approach

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sen, Madhura Rajasekaran, Rajkumar Jayaram Reddy, A. Govinda, K. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350306415

Genetic diseases are conditions caused by a spontaneous alteration or mutation in an individual's DNA. People can inherit genetic disorders from parents, which means they are born with them, even if they are not initially noticeable. On the other hand, some illnesses are not inherited and arise due to disease-causing mutations occurring during cell division. In link mining, link prediction is a crucial task. Based on attribute information and observed existing link information, link prediction predicts if there will be a link between two nodes. Link prediction is applicable not just in the sphere of social networks but also in other fields. This project primarily aims to forecast a person's probability of being diagnosed with a genetic condition. To accomplish this, elements such as the person's family history, the environment in which they live, and a variety of others will be considered. The likelihood of a person being diagnosed with a specific genetic condition can be predicted using the firefly algorithm. If a person is genetically connected to someone having a given ailment, they are more likely to be diagnosed. Pollution, radioactive radiation, and other environmental factors may also affect the disease's incidence. Considering these factors, we suggest a method for determining the likelihood of a person being diagnosed via link prediction. © 2024 IEEE.

关键词： firefly algorithm genetic disorders healthcare link prediction social network analysis

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Enterprise Performance Prediction Based on FASSA Elman Neural Network 3

Enterprise Performance Prediction Based on FASSA Elman Neura...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Wang, Enhua Claro M. Recto Academy of Advanced Studies Lyceum of the Philippines University Manila Manila Philippines

ISBN: (纸本)9798350380989

The Elman Neural Network, known for its distinctive architecture with recurrent connections, is a tool for dynamic modeling and capturing temporal dependencies. To enhance its capabilities, this paper introduces an innovative optimization approach, addressing challenges related to random weight initialization. We propose an enhanced Elman Neural Network optimization strategy using the Sparrow Search algorithm (SSA), recognizing its global search capabilities and improved precision. To overcome SSA's drawbacks, a novel firefly Optimized Sparrow Search algorithm is introduced, integrating the firefly algorithm to enhance optimization complexity. The FA-SSA optimized Elman Neural Network is applied to predict enterprise performance, showcasing benefits in capturing temporal dynamics and intricate patterns. In summary, the proposed firefly Optimized Sparrow Search algorithm offers a robust solution for systematic weight and threshold optimization, enhancing the Elman Neural Network's performance. © 2024 IEEE.

关键词： Elman Neural Network firefly algorithm performance prediction Sparrow Search algorithm

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Nature Inspired Data Mining algorithm for Document Clustering in Information Retrieval 1

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10th Asia-Information-Retrieval-Societies Conference (AIRS)

作者： Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza Univ Utara Malaysia Coll Arts & Sci Sch Comp Sintok 06010 Kedah Malaysia Univ Technol Baghdad Informat & Commun Technol Ctr Baghdad Iraq

ISBN: (数字)9783319128443

ISBN: (纸本)9783319128443;9783319128436

Document clustering is an important technique that has been widely employed in Information Retrieval (IR). Various clustering techniques have been reported, but the effectiveness of most techniques relies on the initial value of k clusters. Such an approach may not be suitable as we may not have prior knowledge on the collection of documents. To date, there are various swarm based clustering techniques proposed to address such problem, including this paper that explores the adaptation of firefly algorithm (FA) in document clustering. We extend the work on Gravitation firefly algorithm (GFA) by introducing a relocate mechanism that relocates assigned documents, if necessary. The newly proposed clustering algorithm, known as GFA_(R), is then tested on a benchmark dataset obtained from the 20Newsgroups. Experimental results on external and relative quality metrics for the GFA_(R) is compared against the one obtained using the standard GFA and Bisect K-means. It is learned that by extending GFA to becoming GFA_(R), a better quality clustering is obtained.

关键词： firefly algorithm text clustering document clustering

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Optimized ELM-based Fault Diagnosis Method for Spare Power Automatic Switching System 3

Optimized ELM-based Fault Diagnosis Method for Spare Power A...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Lv, Dong Yao, Hong Liu, Xiaolin Wu, Linjie Ma, Yichun Ji, Yunlu Gong, Tao Suzhou Industrial Park Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd. Suzhou China

ISBN: (纸本)9798350393224

The spare power automatic switching system is vital to guarantee the stability and security for the power supple in the electrical power system. Therefore, the fault diagnosis of the spare power automatic switching system is a hot research topic. In this paper, we mainly focus on the fault diagnosis method based on the extreme learning machine (ELM) for the spare power automatic switching system. Thereinto, the novel intelligent optimization algorithm, i.e., the firefly algorithm (FA), is used to optimize the ELM. Therefore, the proposed optimized-ELM model can effectively detect the faults in the spare power automatic switching system. In the simulation section, the fault diagnosis accuracy of the optimized-ELM model can reach to 97.85%. The simulation results prove the effectiveness of the optimized ELM of our paper. © 2023 IEEE.

关键词： Extreme learning machine Fault diagnosis firefly algorithm Spare power automatic switching system

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