检索结果-内蒙古大学图书馆

Maiden Application of Meta-Heuristic Techniques with Optimized Integral minus Tilt-Derivative Controller for AGC of Multi-area Multi-Source System

引用

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2020年第28期7卷 1-9页

作者： Bhagat, Sanjeev Kumar Babu, Naladi Ram Sakia, Lalit Chandra Raju, Dhenuvakonda Koteswara Natl Inst Technol Silchar Silchar 788010 Assam India

This article presents a new meta-heuristic algorithm. optimized secondary controller called Integral minus Tilt-Derivative (I -TD) for automatic generation control of three area multi -source system. Area-1 comprises of thermal and solar thermal units, area-2 comprises of two thermal units and area-3 comprises of thermal and wind systems. Comparison of system responses using proposed I-TD controller and some other commonly used controller revels better dynamics characteristics of the proposed one. Dynamic responses of the system corresponding to various meta heuristic optimization technique like firefly algorithm (FA), grey-wolf optimization (GWO), grass -hopper algorithm (GHA) explore that GHA provides slightly better dynamics than the other and also converges faster. Further, sensitivity analysis suggests that system dynamics with GHA optimized I-TD controller at various loading conditions are robust and are not reset again.

关键词： automatic generation control firefly algorithm grass-hopper algorithm grey-wolf optimization and sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Handwritten optical character recognition by hybrid neural network training algorithm

引用

The Journal of Photographic Science 2019年第7期67卷 359-373页

ABSTRACTABSTRACTHandwritten optical character recognition (OCR) is the renowned research area in several fields, like writers identification, bank cheques, and so on. Literature works presented the handwritten OCR for various languages. This paper proposes a hybrid neural network training algorithm for English handwritten OCR. Initially, the noise in the input image is removed using the median filter, and the image is resized. Then, the feature sets, positional, and structural descriptors are extracted from the input image. Once the feature sets are extracted, the proposed FLM-based neural network identifies the handwritten character. The FLM proposed by combining the firefly and the Levenberg–Marquardt (LM) algorithm for training the neural network. Finally, the proposed FLM-based neural network is integrated within the feed forward neural network, and the classification of character is done with 95% accuracy based on the size of training data, number of hidden neurons and number of hidden layers.

关键词： FLM based neural network H-descriptors D-descriptors firefly algorithm Levenberg–Marquardt algorithm Optical Character Recognition median filter Feed forward neural network

来源：评论

学校读者我要写书评

暂无评论

Prediction of fatigue crack propagation based on dynamic Bayesian network

引用

ADVANCES IN MECHANICAL ENGINEERING 2022年第11期14卷

作者： Wang, Wei Yang, Yanfang Li, Mengzhen Liu, Weikai Liu, Zhiping Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Peoples R China Minist Educ Engn Res Ctr Port Logist Technol & Equipment Wuhan Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn 1178 Heping Ave Wuhan 430063 Hubei Peoples R China

To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper. The Paris Law of crack propagation and the extended finite element method (XFEM) are combined to establish the state equation of crack propagation. The uncertain factors of crack propagation are analyzed, and the prediction model of fatigue crack propagation based on the dynamic Bayesian network is constructed. A Bayesian inference algorithm based on the combination of Gaussian particle filter and firefly algorithm is proposed. The fatigue experiment of the specimen with the pre-cracks is carried out to test the correlation between the fatigue load cycles and the crack propagation depth. The experimental results show that the crack propagation prediction method proposed in this paper can effectively improve the prediction accuracy of crack propagation depth.

关键词： Fatigue crack propagation dynamic Bayesian network Gaussian particle filter firefly algorithm the extended finite element method

来源：评论

学校读者我要写书评

暂无评论

OPTIMIZATION algorithm FOR URBAN RAIL TRANSIT OPERATION SCHEDULING BASED ON LINEAR PROGRAMMING

引用

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2023年第3期24卷 203-216页

作者： Wu, Shuang Wu, Jinlong Sun, Yifeng Yao, Tong Yangzhou Polytech Inst Dept Transportat Engn Yangzhou 225000 Peoples R China Suzhou Sip Ourlead Indudtrial Equipment Co Ltd Suzhou 215000 Peoples R China Sichuan Tourism Univ Sch Econ & Management Chengdu 610100 Peoples R China

At present, the traditional urban public transportation system cannot meet people's daily travel needs. Urban Rail Transit (URT) has been rapidly promoted in major cities due to its advantages such as low energy consumption, high frequency, and large traffic volume. To achieve a more excellent and energy-saving operation scheduling strategy, the research first combines the train dynamics model and the energy consumption model. Since the optimization problem of URT is a linear problem, the attraction model of the firefly algorithm can determine the calculation time consumed by the algorithm, which is very suitable for the complex optimization problem of URT. Therefore, the FA based optimization algorithm for urban rail transit operation scheduling (FURTOSO) based on the firefly algorithm is studied and designed. Therefore, based on the study of the four working conditions of traction, cruise, coasting, and braking, a firefly algorithm for Urban Rail Transit Operation Scheduling (FURTOSO) was designed. Finally, the study optimizes the operation scheduling of Chengdu Metro Line 8 from two aspects: driving strategy and train schedule. The research demonstrates that the FURTOSO algorithm only needs 76 iterations to reach a stable state, with a fitness value of 0.6827. In practical applications, the utilization rate of train RBE is 30.1%, the total energy consumption (TEC) is 2.661 * 1011J, and the energy saving rate is 13.03%. In summary, the FURTOSO algorithm proposed in the study has excellent performance and has better energy-saving effects in Chengdu Metro Line 8.

关键词： Urban Rail Transit Scheduling Optimization Regenerative Braking firefly algorithm Energy Conservation

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

引用

algorithmS 2017年第4期10卷 120-120页

作者： Saad, Abdulbaset El Hadi Dong, Zuomin Karimi, Meysam Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada

Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and black-box forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), firefly algorithm (FFA), Cuckoo Search (CS), Bat algorithm (BA), Flower Pollination algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

关键词： nature based optimization artificial bee colony firefly algorithm cuckoo search bat algorithm flower pollination algorithm grey wolf optimizer

来源：评论

学校读者我要写书评

暂无评论

Evaluation model and algorithm optimization of intelligent manufacturing system on the basis of BP neural network

引用

INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 20卷

作者： Tian, Wei Cao, Yu Hulunbuir Univ Engn Coll Hulunbuir 021008 Peoples R China Engn Res Ctr Safe Exploitat & Comprehens Utilizat Hulunbuir 021008 Peoples R China Hulunbuir Coll Agr & Husb Machinery Res Inst Hulunbuir 021008 Peoples R China

Intelligent manufacturing is a key direction for the development of the manufacturing industry. Since the level of intelligent manufacturing varies among enterprises, building a scientific and reliable evaluation model is the key to evaluate enterprises. The study optimizes the Back Propagation (BP) neural network based on the firefly algorithm (FA) and innovatively introduces the Sparrow Search algorithm (SSA), which changes the search mechanism of the BP and improves the traditional BP affected by the local optimum. of the traditional BP. In the experimental results, the average error of the improved model is 1.34 % and 0.41 % under the optimal preset parameters, which is significantly better than other advanced algorithms. It is verified that the improved evaluation model in this study has lower error and better effect, which has better application value in the evaluation of intelligent manufacturing system.

关键词： IM firefly algorithm BP neural network Sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

The Calculation of Sports Injury Sensitivity of Martial Arts Athletes Based on ROC Curve

引用

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期8卷 3009-3018页

作者： Wang, Qinglong Sui, Yong Hebei Univ Water Resources & Elect Engn Cangzhou 061001 Peoples R China

Martial arts is a comprehensive sport. It requires the athlete to have speed, explosiveness, and coordination. Injuries are common among athletes in martial arts training. In this paper, the mathematical statistics method of the ROC curve is used to predict athletes' sports injuries. At the same time, the author uses the firefly algorithm to establish the relevant factors for evaluating the degree of foot and ankle injury of martial arts athletes. In this way, the fitness function of the damage degree evaluation index is obtained. Finally, this paper verifies the effectiveness of the method by computer simulation. The mathematical statistics method of the ROC curve and the firefly algorithm-based injury evaluation method for martial arts players have particular guiding significance for improving the technical ability of Chinese martial arts players.

关键词： Martial arts Sports injuries ROC curve firefly algorithm Data analysis

来源：评论

学校读者我要写书评

暂无评论

A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach

引用

FUTURE INTERNET 2020年第11期12卷 192页

作者： Azzoug, Youcef Boukra, Abdelmadjid Soares, Vasco N. G. J. Univ Sci & Technol Houari Boumediene Dept Informat Algiers 16111 Algeria Inst Telecomunicacoes P-6201001 Covilha Portugal Polytech Inst Castelo Branco P-6000084 Castelo Branco Portugal

The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the firefly algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle's destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA-GSO approach. The proposed FA-GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.

关键词： VDTNs next-SCF vehicle selection ProPHET probabilistic DTN routing Glowworm Swarm Optimization firefly algorithm swarm-based approaches

来源：评论

学校读者我要写书评

暂无评论

An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN

引用

FUTURE INTERNET 2019年第12期11卷 252页

作者： Li, Guoyan Li, Kaixin Liu, Yi Pan, Yuheng Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China

Static multi-controller deployment architecture cannot adapt to the drastic changes of network traffic, which will lead to a load imbalance between controllers, resulting in a high packet loss rate, high latency, and other network performance degradation problems. In this paper, an efficient dynamic load balancing scheme based on Nash bargaining is proposed for a distributed software-defined network. Firstly, considering the connectivity of network nodes, the switch migration problem is transformed into a network mapping relationship reconstruction problem. Then, we establish the Nash bargaining game model to fairly optimize the two contradictory goals of migration cost and load balance. Finally, the model is solved by an improved firefly algorithm, and the optimal network mapping state is obtained. The experimental results show that this scheme can optimize the migration cost and load balance at the same time. Compared with the existing research schemes, the migration process of the switch is optimized, and, while effectively balancing the load of the control plane, the migration cost is reduced by 14.5%.

关键词： software-defined networking load balance switch migration Nash bargaining firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid Metaheuristics for Job Shop Scheduling Problems

引用

ENGINEERING LETTERS 2022年第4期30卷 1444页

作者： Nugraheni, Cecilia E. Swastiani, D. Abednego, L. Parahyangan Catholic Univ Fac Informat Technol & Sci Informat Dept Bandung Indonesia Bandung Inst Technol Majoring Informat Bandung Indonesia

Job Shop Scheduling (JSS) is one of the problems in the production process. The sequence of operation and processing time are often different because some jobs consist of multiple processes, with each being performed by a different machine. The purpose of JSS is to determine the order in which jobs are processed to meet specific optimization criteria. Several solutions to Job Shop Scheduling Problem (JSSP) have been proposed, either with exact approaches or heuristics. It was observed that one of the most widely used heuristic approaches is metaheuristics, specifically the Genetic algorithm, which has the advantage of finding solutions globally, also known as global optimal. However, this algorithm is often trapped in a search that only involves local optimal values. This study proposes an approach to improve the Genetic algorithm performance by combining it with another metaheuristic, known as the firefly algorithm, which has the advantage of finding solutions locally or called local optimal. It is, therefore, possible to maintain global and local optimal balance and obtain better performance by combining these two algorithms as well. Furthermore, two approaches are proposed, which include S-GAFA and C-GAFA. Several experiments were performed to measure these proposed algorithms. It was observed that S-GAFA and C-GAFA performed better than the Genetic algorithm in solving JSSP.

关键词： firefly algorithm Genetic algorithm Job Shop Scheduling Problem metaheuristic optimization

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：