检索结果-内蒙古大学图书馆

8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

作者： Xu, Yan Yao, Xiongkai Jiangxi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang Peoples R China

ISBN: (纸本)9781665455336

Parameters have always been the core part of an algorithm, but good parameters are not immutable. They change as the problem changes, so how to set the optimal parameters according to the size of the problem has always been a difficult problem in the field of algorithm. Traditional ant colony algorithm has some problems such as difficulty in determining initial parameters and falling into local optimization. A hybrid ant colony algorithm for searching initial parameters was proposed. The optimal parameter array of the ant colony algorithm is obtained by introducing the firefly algorithm (FA) to optimize the initial parameters. The hybrid ant colony algorithm after parameter optimization to solve the traditional traveling salesman problem (TSP) is used. The results show that the running time and shortest tour path of the hybrid ant colony algorithm with the calculated parameters are shorter. In the last, the number of cities is continuously reduced to repeatedly search the optimal parameters under different difficulty problems. The experimental results have certain reference significance for the follow-up research of the hybrid ant colony algorithm.

关键词： traveling salesman problem ant colony algorithm firefly algorithm parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Fault-tolerant optimal pitch control of wind turbines using dynamic weighted parallel firefly algorithm

引用

ISA TRANSACTIONS 2022年第PartA期128卷 301-317页

作者： Mousavi, Yashar Bevan, Geraint Kucukdemiral, Ibrahim Beklan Glasgow Caledonian Univ Dept Appl Sci Sch Comp Engn & Built Environm Glasgow G4 0BA Lanark Scotland

With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional-integral-derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fault-tolerant control Wind turbine Pitch control Optimization firefly algorithm Fractional calculus

来源：评论

学校读者我要写书评

暂无评论

Power aware virtual machine placement in IaaS cloud using discrete firefly algorithm

引用

APPLIED NANOSCIENCE 2022年第3期13卷 2003-2011页

作者： Balaji, K. Kiran, P. Sai Kumar, M. Sunil Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India Sree Vidyanikethan Engn Coll Autonomous Dept Comp Sci & Engn Tirupati AP India

Cloud Computing is a widely adopted computing model that offloads the in-house processing workloads to remote servers. In recent years, the adoption of cloud computing and related models have increased multifold. The cloud data center consumes an enormous amount of electricity and becomes a major issue for emitting greenhouse gases. The most important power conservation strategy used in IaaS cloud is scheduling the virtual machine appropriately into the physical servers to minimize the number active servers. As the number of active servers decreases, the power consumption of a data center will also decrease. The fundamental aim of the proposed work is to schedule the virtual machine as dense as possible in a minimal number of servers using the proposed modified discrete firefly algorithm for power consumption. The proposed algorithm will effectively explore the large search space to find a placement that uses minimal power consumption in the data centers. The proposed algorithm is executed to place virtual machines of various configurations in IaaS cloud and the results are compared with Genetic algorithm and Particle Swarm Optimization shows its superiority.

关键词： Virtual machine placement firefly algorithm Cloud computing Scheduling

来源：评论

学校读者我要写书评

暂无评论

Optimal coordination of directional overcurrent relay based on combination of firefly algorithm and Linear Programming

引用

AIN SHAMS ENGINEERING JOURNAL 2022年第6期13卷

作者： Ramli, Suzana Pil Mokhlis, Hazlie Wong, Wei Ru Muhammad, Munir Azam Mansor, Nurulafiqah Nadzirah Univ Malaya Fac Engn Dept Elect Engn Malaysia Kuala Lumpur Malaysia Iqra Univ Dept Elect Engn Main Campus Karachi 75300 Pakistan

Optimal coordination and settings of directional overcurrent relays (DOCRs) are crucial task to ensure reliable and effective protection in power system networks. The settings of DOCRs are plug setting current (PSC) and time multiplier setting (TMS). The DOCRs coordination is well-known as a highly constrained nonlinear optimization problem. Its complexity in terms of non-linearity increases along with the network size increment. This paper proposes a hybrid optimization algorithm which consists of firefly algorithm and Linear Programming (FA-LP) to relax the search space by linearizing the equation of the DOCRs coordination to attain an optimal solution. Furthermore, this paper also considers a mixed type of IEC relay characteristics to attain effective relay operation. The proposed method is tested on the IEEE 8-, 15- and 30-bus test systems. The results show the reduction of total relay operating time between 15.6% and 85.5% as compared to other techniques in the literature and also being verified using the ETAP software.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： Directional overcurrent relay Hybrid optimization Protection Linear programming firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing firefly algorithm with sliding window for continuous optimization problems

引用

NEURAL COMPUTING & APPLICATIONS 2022年第16期34卷 13733-13756页

作者： Peng, Hu Qian, Jiayao Kong, Fanrong Fan, Debin Shao, Peng Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China JiangXi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

firefly algorithm (FA) is a simple and effective swarm intelligence algorithm, which has received wide attention from scholars. In original FA, each firefly must be compared with other fireflies in brightness, but it may not move, which may result in waste of system resources. Therefore, an enhancing firefly algorithm with sliding window (SWFA) is proposed in this paper to address the above problem. SWFA introduces sliding window mechanism to improve the attraction model of the FA, which is a technology used to ensure the reliability of data transmission in computer networks. The sliding window mechanism is essentially an archive mechanism, where the window denotes a form of archive, and sliding is the way the window updates. The update of the population is guided through the method of information exchange among individuals inside and outside the window. SWFA also combines the sliding window mechanism with reverse learning to reduce the number of comparisons and ensure every comparison is effective. Moreover, a novel adaptive step adjustment strategy is designed, which balances exploration and exploitation of FA. In order to verify the effectiveness of SWFA, extensive experiments are conducted on the CEC 2015 and CEC2013 test suite. Additionally, experiments are conducted on parameters estimation of chaotic systems and three practical engineering optimization problems. The results of the experiments show that the proposed algorithm has better performance.

关键词： firefly algorithm Swarm intelligence algorithm Attraction model Sliding window

来源：评论

学校读者我要写书评

暂无评论

Enhancing firefly algorithm with adaptive multi-group mechanism

引用

APPLIED INTELLIGENCE 2022年第9期52卷 9795-9815页

作者： Cao, Lianglin Ben, Kerong Peng, Hu Zhang, Xian Naval Univ Engn Coll Elect Engn Wuhan 430033 Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China

firefly algorithm (FA) is efficient in solving continuous optimal problems, because of its ability to a global search. However, the redundant attractions and incorrect directions may reduce the efficiency of FA. To improve the performance of FA, a novel multi-group mechanism is proposed based on an assumption that firefly has a visual field. The modified firefly algorithm is called the visual firefly algorithm(VFA). The framework of VFA combines the assumption with the designed strategies to balance the exploration and exploitation. Where the proposed observer strategy works for the exploration, the suggested selective random strategy plays the role of the exploiter. To verify the performance of the presented algorithm, extensive experiments are executed on CEC2013 benchmark functions. Additionally, the efficiency of the proposed multi-group mechanism is analyzed in-depth. The experimental results reveal that the proposed multi-group mechanism improves FA and provides a suitable solution for most CEC2013 problems with different dimensions. Especially, its performance remains robust, where the problems become more complex.

关键词： Optimization firefly algorithm Multi-group Efficiency

来源：评论

学校读者我要写书评

暂无评论

INVESTIGATION OF MACHINABILITY PERFORMANCE IN TURNING OF Ti-6Al-4V ELI ALLOY USING firefly algorithm AND GRNN APPROACHES

引用

SURFACE REVIEW AND LETTERS 2022年第6期29卷 2250075-2250075页

作者： Kumar, Ramanuj Pandey, Anish Sahoo, Ashok Kumar Rafighi, Mohammad KIIT Univ Sch Mech Engn Bhubaneswar 751024 Odisha India Univ Turkish Aeronaut Assoc Dept Mech Engn TR-06790 Ankara Turkey

Ti-6Al-4V ELI alloy is one of the most familiar materials for orthopedic implants, aeronautical parts, marine components, oil and gas production equipment, and cryogenic vessel applications. Therefore, its appropriate quality of finishing is highly essential for these applications. But the characteristics like lower modulus of elasticity, lesser thermal conductivity, and high chemical sensitivity placed it in the categories of difficult-to-cut metal alloys. Also, tooling cost is one of the prime issues in the machining of this alloy. Therefore, this research is more inclined to use a low-budget uncoated carbide tool in turning the Ti-6Al-4V ELI alloy. Also, the selection of suitable levels of machining parameters is highly indispensable to get the appropriate surface finish with a low tooling cost. So, the L-16 experimental design is utilized to check the performances of the uncoated carbide tool in the turning tests. The performance indexes like surface roughness (Ra), flank wear of tool (VBc), and material removal rate (MRR) are measured and studied with the help of surface plots and interaction plots. Further, the firefly algorithm optimization is employed to find the optimal cutting parameters and cutting response values. The local optimal values of the input parameters a, f, and V-c are estimated as 0.3241 mm, 0.0893 mm/rev, and 82.41 m/min, respectively. Similarly, the global optimal values for the responses Ra, VBc, and MRR are reported as 0.6321 mu m, 0.09253 mm, and 24.61 g/min, individually. Additionally, to predict the responses, Generalized Regression Neural Network (GRNN) modeling is employed and the average absolute error for each response is noticed to be less than 1%. Therefore, the GRNN modeling tool is strongly recommended for various machining applications.

关键词： Ti-6Al-4V ELI surface roughness flank wear firefly algorithm GRNN

来源：评论

学校读者我要写书评

暂无评论

A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

引用

KNOWLEDGE-BASED SYSTEMS 2022年 241卷 108225-108225页

作者： Ta Bao Thang Huynh Thi Thanh Binh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

The clustered minimum routing cost tree (CluMRCT) problem is a recent problem with a wide range of real-life applications, especially in designing computer networks with peer-to-peer architecture. Many multifactorial evolutionary algorithms have been proposed to solve multiple CluMRCT problems simultaneously. However, these algorithms only function effectively on complete graphs with smalland-medium sizes. Moreover, the blindness and randomness in the transfer of genetic materials cause a reduction in the exploitation ability and make these algorithms ineffective to solve low-similarity tasks. This paper proposes a hybrid multitasking algorithm named multifactorial firefly algorithm, which integrates the firefly algorithm's strong exploitation ability to enhance the self-evolution of each task when facing low-similarity tasks while improving inter-task knowledge transfers by delivering higherquality solutions. Also, the proposed algorithm is equipped with new encoding and decoding to focus more on potential search areas on both complete and sparse graphs. The experiments and Wilcoxon signed-rank tests were conducted on various instances to verify our proposal with several state-of-theart methods. The results portrayed that the proposed encoding scheme helped multitasking algorithms improve solution quality by 32% on average. Besides, the statistical test values proved the superiority of the proposed hybrid algorithm in terms of solution quality and convergence trend. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Multifactorial evolutionary algorithm firefly algorithm Hybrid algorithm Minimum routing cost Clustered Tree Problem

来源：评论

学校读者我要写书评

暂无评论

An Effective Sustainable Control of Brushless DC Motor using firefly algorithm - Artificial Neural Network based FOPID Controller

引用

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartB期52卷

作者： Kommula, Bapayya Naidu Kota, Venkata Reddy Aditya Engn Coll A Dept Elect & Elect Engn Surampalem India Jawaharlal Nehru Technol Univ Kakinada Dept Elect & Elect Engn Kakinada India

This paper proposes a Fractional purchase PID (FOPID) controller based on firefly algorithm-Artificial Neural Network (FA-ANN) to effectively control both the speed and torque of Brushless DC Motor (BLDCM). (BLDCM). The traditional PID controller produces sluggish response and is not efficient. In order to overcome the demerits associated with typical controller, an intelligent FOPID controller is proposed. A Modified firefly algorithm (FA) is implemented to attain ideal gain parameters of the proposed controller. To enhance MFF performance, the randomized parameters are updated by ANN. The proposed controller is simulated in Matlab/Simulink platform. The functional analysis of this proposed controller is demonstrated and compared with the existent schemes namely genetic algorithm GA-ANN and FA techniques. The experimental prototype with the intended technique is designed at the same time, and the results of the experiments are verified.

关键词： BLDC motor firefly algorithm Artificial Neural Network FOPID Controller Power generation Sustainability

来源：评论

学校读者我要写书评

暂无评论

Cluster head selection using hesitant fuzzy and firefly algorithm in wireless sensor networks

引用

EVOLVING SYSTEMS 2022年第1期13卷 65-84页

作者： Rayenizadeh, Mojgan Rafsanjani, Marjan Kuchaki Saeid, Arsham Borumand Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran Shahid Bahonar Univ Kerman Fac Math & Comp Dept Pure Math Kerman Iran

Nowadays, wireless sensor networks (WSNs) are used to monitor and collect data in various environments. One of the main challenges in WSNs is the energy consumption due to the deployed sensor nodes in WSNs are energy-constrained. Clustering method is a solution for this problem and the cluster head (CH) selection process is a major part of the clustering method. This paper used the firefly algorithm (FA) and hesitant fuzzy to propose a new CH selection protocol. The proposed protocol uses three parameters of sensor nodes to calculate the score of each node to determine the best CHs. In order to describe the performance of the proposed protocol, three scenarios are simulated and evaluated. The simulation results show that the proposed protocol improves the energy saving and increases the network lifetime.

关键词： Wireless sensor networks Clustering Cluster head selection Hesitant fuzzy firefly algorithm

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：