检索结果-内蒙古大学图书馆

Financial warning for coal mining investments: Evidence from the fruit fly optimisation algorithm with backpropagation neural networks

引用

ENERGY ECONOMICS 2024年 134卷

作者： Ren, Xiaocong Huang, Zilong He, Yiqun Liaoning Univ Sch Econ Shenyang 110036 Liaoning Peoples R China

Venture capital firms may be unable to withstand the high investments and risks associated with coal mining exploration due to limitations in the funding scale. Risk control capability is also a challenge for venture capital companies. Finding a balance between high-risk and high-return coal mining exploration projects is a problem that venture capital companies must face. We propose a financial risk-warning model for coal mining investment enterprises based on the fruit fly optimisation algorithm (FOA) with the backpropagation neural network (BPNN). The fusion of standardised and dimensionless sample data through factor analysis reduces the input dimension of the BPNN and improves its stability. The financial warning algorithm of the model has been effectively validated through experiments. The research results indicate that its accuracy has reached a high level of financial warning, reaching a level of 95%.

关键词： Coal mining investment Financial risk-warning model Venture capital firms Backpropagation neural network fruit fly optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid fruit fly optimisation algorithm for field service scheduling problem

引用

INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2020年第5-6期14卷 554-570页

作者： Wu, Bin Cheng, Jing Dong, Min Nanjing Tech Univ Sch Econ & Management Nanjing 211816 Peoples R China

The field service scheduling problem model considering the skill level of workers based on the optimisation goals of travel time, service time, and waiting time is present in the paper. A hybrid fruit fly optimisation algorithm (FOA) is proposed to optimise the model. Based on the features of the problem and merit of the algorithm, a matrix encoding method is designed. Three search operators are then proposed, and the smell-based search strategy and vision-based search strategy for the FOA are redesigned. Additionally, an initialisation operator and a post-optimisation process are constructed to improve the performance of the FOA. Finally, the proposed operators and strategies are compared and analysed, and the hybrid FOA is compared with other algorithms through simulation experiments. The simulation results demonstrate that the proposed hybrid fruit fly optimisation algorithm is an effective method to solve the field service scheduling problem.

关键词： field service scheduling problem FSSP fruit fly optimisation algorithm intelligent computing

来源：评论

学校读者我要写书评

暂无评论

Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第22期54卷 6782-6797页

作者： Han, Yuyan Gong, Dunwei Li, Junqing Zhang, Yong China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Peoples R China LanZhou Univ Technol Sch Elect Engn & Informat Engn Lanzhou Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China

The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard's instances are reported for this problem, which can be used as a basis of comparison in future studies.

关键词： Blocking flowshop makespan fruit fly optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

fruit fly optimisation algorithm with adaptive sign processing

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2015年第6期6卷 538-545页

作者： Guo, Xiaodong Zhang, Xueliang Wang, Lifang Taiyuan Univ Sci & Technol Sch Elect & Informat Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Sch Mech Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Engn Taiyuan 030024 Shanxi Peoples R China

Inspired by probability estimation for code-words in adaptive arithmetic coding, a fruit fly optimisation algorithm with adaptive sign processing is presented to solve a class of optimisation problems with negative optimum point. Sign probability of previously optimal solutions are dynamically calculated, and signs of smell concentration judgement value are adaptively created, then drosophila swarms in this way will search more fast and stationary than the ones gained in random manner. Simulation results for test functions show the adaptive manner in sign processing is efficient and potential for fruit fly optimisation algorithm.

关键词： fruit fly optimisation algorithm function optimisation adaptive sign processing random sign processing

来源：评论

学校读者我要写书评

暂无评论

Comparative performance analysis of fruit fly optimisation algorithm for multi-area multi-source automatic generation control under deregulated environment

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第14期9卷 1845-1855页

作者： Mohanty, Banaja Hota, Prakash Kumar Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 Odisha India

A new swarm optimisation algorithm, that is, fruit fly optimisation algorithm is proposed to tuned several classical controllers for multi-area multi-source interconnected power system under deregulated environment. In deregulated environment, for multi-source combination of reheat thermal, hydro and nuclear generating units in each control area with appropriate generation rate constraint and AC/DC link. The performance of several controller such as integral (I), proportional-integral, proportional-integral-derivative, integral-double derivative (IDD) and proportional-integral-double derivative (PIDD) are compared under different power system scenario and it is found that PIDD controller performs better than other controllers. Then sensitivity analysis is performed with system parameter variation from their nominal values. Sensitivity analysis reveals that optimum gains of PIDD controller need not be reset for wide variation in system parameters.

关键词： power system interconnection hydrothermal power systems nuclear power stations power generation control electricity supply industry deregulation optimisation three-term control PI control sensitivity analysis thermal power stations comparative performance analysis fruit fly optimisation algorithm multiarea multisource automatic generation control deregulated environment swarm optimisation algorithm multiarea multisource interconnected power system reheat thermal generating unit hydro generating unit nuclear generating unit generation rate constraint AC-DC link proportional-integral controller integral controller proportional-integral-derivative controller integral-double derivative controller proportional-integral-double derivative controller PIDD controller IDD controller sensitivity analysis system parameter variation

来源：评论

学校读者我要写书评

暂无评论

Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm

引用

KNOWLEDGE-BASED SYSTEMS 2014年第Dec.期72卷 28-36页

作者： Li, Jun-qing Pan, Quan-ke Mao, Kun Suganthan, P. N. Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Nanyang Technol Univ Singapore 639798 Singapore Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper presents an effective fruit fly optimisation algorithm (FOA) to solve the steelmaking casting problem. First, we model the realistic problem as a hybrid flow shop (HFS) scheduling problem with batching in the last stage. Next, the proposed FOA algorithm is applied to solve the realistic HFS problems. In the proposed algorithm, each solution is represented by a fruit fly. Each fruit fly first improves its status through a well-designed smell search procedure. During the vision-based search procedure, the worst fruit fly in the population will be induced by the best fruit fly found thus far to improve the exploitation ability of the entire fruit fly population further. To enhance the exploration ability of the proposed algorithm, in each generation, each fruit fly that has not updated its status during the last several iterations Will be replaced by a newly-generated fruit fly. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Moreover, the influence of the parameter setting is also investigated using the Taguchi method of the design-of-experiment (DOE) to determine the suitable values for the key parameters. The results indicate that the proposed FOA is more effective than the four presented algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Hybrid flow shop scheduling Steelmaking casting problem fruit fly optimisation algorithm Realistic scheduling problem Neighbourhood structure

来源：评论

学校读者我要写书评

暂无评论

Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting

引用

CONNECTION SCIENCE 2019年第3期31卷 244-266页

作者： Chen, Yang Pi, DeChang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China

fruit fly optimisation algorithm is a new swarm intelligence algorithm, which is simple and efficient. However, it is easy to get premature convergence in solving high-dimensional complex continuous functions. In order to overcome the shortcoming and improve the precision of solution, we propose a new fruit fly optimisation algorithm (SEDMFOA) based on spatial expansion and dynamic mutation. It is featured with changing the original constant step size to a focused search method, and embedding the dynamic mutation strategy in the evolution of the algorithm. Furthermore, we employed gauss mapping operation on the best individual to generate new individuals to substitute for those trans-boundary individuals. Finally, the inverse solution to expand the space was designed to develop the durative search ability in the later stage of the algorithm. According to the experimental results of eighteen well-known benchmark functions, the SEDMFOA is efficient and effective. The precision and stability of the approximate solution of SEDMFOA are superior the algorithms proposed in some related literatures. In wind energy research, the new algorithm is applied to optimise extreme learning machines for short-term wind forecasting. Simulation results show that SEDMFOA has better prediction effect than traditional algorithms.

关键词： fruit fly optimisation algorithm evolutionary computation premature convergence function optimisation wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Optimization of Distributed Storage System of Legal Electronic Data Based on Machine Learning

Journal of Network Intelligence

引用

Journal of Network Intelligence 2024年第3期9卷 1297-1314页

作者： Han, Rong-He Han, Yi-Ping Law School Fujian University of Technology Fuzhou350118 China The University of Manchester ManchesterM139PL United Kingdom

With the advent of the big data era, the volume of electronic data in the legal service system grows dramatically, while the amount of data access gradually increases, resulting in slower and slower response times for system functions. Therefore, to solve the response time problem of legal electronic data, this work proposes a performance tuning method based on distributed storage system. Firstly, the distributed storage features are screened twice to obtain the set of parameters that affect the storage performance. In the process of performing training sample generation, orthogonal experimental design method is used to select representative feature samples for experiments to obtain the experimental values of throughput and latency. Then, the training samples are preprocessed, and the parameter samples and experimental values are combined as the sample data for the prediction model. An integrated neural network model based on Kernel Density Estimation (KDE) is proposed for the problem of sample data containing unbiased noise. The integrated neural network model is used to train and learn from the sample data, and further feature selection is performed to obtain throughput and delay prediction models. Finally, according to the characteristics of the throughput and delay prediction models, multiple swarms co-evolutionary approach is used to improve the position updating method of the traditional fruit fly optimisation algorithm. The performance of the distributed storage system is optimised using the hybrid fruit fly optimisation algorithm, and the optimal solution as well as the corresponding optimal parameter configurations are obtained. Experiments are conducted using four typical workloads to verify the accuracy of the proposed prediction model. The results show that the proposed tuning method can effectively improve the performance of the distributed storage system for legal electronic data. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： big data feature selection fruit fly optimisation algorithm integrated neural network legal service system

来源：评论

学校读者我要写书评

暂无评论

Deep belief network with FOA-based cooperative spectrum sensing in cognitive radio network

引用

INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2021年第3期27卷 323-347页

作者： Sonti, Siva Reddy Prasad, Mutchakayala Siva Ganga Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vijayawada 520002 Andhra Pradesh India

The artificial neural network (ANN) is proposed to the cooperative spectrum sensing (CSS) at CR network. ANN has the drawback that the training of ANN with many hidden layers on large amount of data can affect the performance of the network and optimisation of ANN parameters is a challenging task. To overcome the above drawbacks, the deep belief network (DBN) and fruit fly optimisation algorithm (FOA) are employed. The DBN has four parameters on learning step: learning rate, weight decay, penalty parameter, number of hidden units. These parameters should be properly selected for the proper functioning of DBN. Tuning of these parameters is taken into an optimisation issue and it is addressed by FOA. The proposed method has three steps: training, validation and testing. The metrics used for the performance evaluation are accuracy, false alarm rate and loss detection.

关键词： cognitive radio CR cooperative spectrum sensing CSS deep belief network DBN fruit fly optimisation algorithm FOA

来源：评论

学校读者我要写书评

暂无评论

A novel parametric model for nonlinear hysteretic behaviours with strain-stiffening of magnetorheological gel composite

引用

COMPOSITE STRUCTURES 2023年第1期318卷

作者： Zhang, Guang Chen, Junyu Zhang, Zheng Sun, Min Yu, Yang Wang, Jiong Cai, Shibo Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Zhejiang Univ Technol Key Lab Special Purpose Equipment & Adv Proc Techn Minist Educ & Zhejiang Prov Hangzhou 310014 Peoples R China XGM Corp Ltd Taizhou 317100 Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Ultimo 2007 Australia Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

Magnetorheological (MR) gels are a new class of MR composite materials that can effectively address the issue of particle agglomeration in MR fluids, enhancing the controllability of materials in engineering applications. In this study, a novel parametric model was developed to track the nonlinear hysteretic behaviours with strain -stiffening of the MR gel. The model parameters were identified using the measurement data pertaining to five current levels (0A, 0.2A, 0.5A, 0.8A, and 1A) under a strain amplitude of 10% and a frequency of 0.1 Hz. The fruit fly optimisation algorithm (FOA) was used to determine the optimal solution for the model parameters. The model parameters were generalised with respect to the loading current, and the reliability of the generalised model was verified. The results of the study demonstrate that the proposed model outperforms two classical models, the Bouc-Wen model and viscoelastic-plastic model, in capturing the nonlinear and strain-stiffening behaviour of MR gels, particularly for higher current excitations. This study has potential applications in pre-dicting the nonlinear hysteresis behaviour of automotive dampers, and provides a theoretical basis for the semi -active control of suspensions.

关键词： Magnetorheological gel composite Parametric model Strain -stiffening Nonlinear hysteretic loop fruit fly optimisation 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：