检索结果-内蒙古大学图书馆

A new fruit fly optimization algorithm: Taking the financial distress model as an example

KNOWLEDGE-BASED SYSTEMS 2012年 26卷 69-74页

作者： Pan, Wen-Tsao Oriental Inst Technol Dept Informat Management New Taipei City Taiwan

The treatment of an optimization problem is a problem that is commonly researched and discussed by scholars from all kinds of fields. If the problem cannot be optimized in dealing with things, usually lots of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with the complicated optimization method proposed by past scholars is proposed: the fruit fly optimization algorithm. In this article, throughout the process of finding the maximal value and minimal value of a function, the function of this algorithm is tested repeatedly, in the mean time, the population size and characteristic is also investigated. Moreover, the financial distress data of Taiwan's enterprise is further collected, and the fruit fly algorithm optimized General Regression Neural Network, General Regression Neural Network and Multiple Regression are adopted to construct a financial distress model. It is found in this article that the RMSE value of the fruit fly optimization algorithm optimized General Regression Neural Network model has a very good convergence, and the model also has a very good classification and prediction capability. (C) 2011 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Financial distress optimization problem General Regression Neural Network Data mining

来源：评论

学校读者我要写书评

暂无评论

Research on Path Planning of AGV Based on Improved Ant Colony optimization algorithm 33

Research on Path Planning of AGV Based on Improved Ant Colon...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Sun, Jiuxiang Yu, Ya'nan Xin, Ling Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Peoples R China

ISBN: (纸本)9781665440899

Path planning is a key problem in the motion control of mobile robot. In order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency, this paper puts forward a matrix yard storage mode, which is transformed into grid map model, and then uses ant colony optimization algorithm to plan the path of AGV. Aiming at the shortcomings of traditional ant colony optimization algorithm (ACO) in global path planning, such as slow convergence speed and weak optimization ability, an improved ant colony path planning algorithm is proposed. Firstly, the grid map is established, and the fruit fly optimization algorithm (FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm, and then the ant colony optimization algorithm is used for global path planning. At the same time, in order to solve the problem of many path turning angles and large cumulative turning angles in the planning, the path smoothing is carried out. The simulation results show that the improved algorithm has fewer turns and smoother path, and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

关键词： Ant colony optimization algorithm fruit fly optimization algorithm matrix yard storage mode Smoothing

来源：评论

学校读者我要写书评

暂无评论

Coal and Gangue Recognition based on Improved Support Vector Machine

Coal and Gangue Recognition based on Improved Support Vector...

引用

International Conference on Computer Vision, Application, and Design (CVAD)

作者： Sun, Yu Xie, Yehui Zhang, Jincai Xian Univ Sci & Technol Xian Peoples R China

ISBN: (数字)9781510651876

ISBN: (纸本)9781510651876;9781510651869

In the process of coal mining, the separation of coal and gangue is a very important step. Traditional coal preparation methods include manual coal preparation, heavy medium coal preparation, ray projection coal preparation, etc. these methods can not separate coal and gangue under the condition of safety and speed at the same time. Therefore, to improve the recognition rate of coal gangue separation, a coal gangue recognition method based on improved Support Vector Machine is proposed in this paper. First, the images of the coal and gangue are preprocessed. Then, the gray and texture features of the coal and gangue are extracted from the preprocessed images. Finally, each feature vector is input into the Support Vector Machine model optimized by fruit fly for recognition and classification. The experimental results show that the accuracy is 96.33%.

关键词： Support Vector Machine Coal and Gangue Recognition fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

引用

JOURNAL OF ENGINEERED FIBERS AND FABRICS 2022年 17卷

作者： Jiang, Hao Song, Jiuxiang Zhang, Baowei Wang, Yonghua Zhengzhou Univ Light Ind 5 Dongfeng Rd Zhengzhou 450002 Henan Peoples R China

Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiveness. The experimental results show that the generalized regression neural network optimized by gray wolf optimizer has the best effect and the fastest optimization speed;the generalized regression neural network optimized by particle swarm optimization algorithm has the middle optimization speed;the generalized regression neural network optimized by fruit fly optimization algorithm has the worst effect and the slowest optimization speed.

关键词： Yarn unevenness prediction few sample dataset generalized regression neural network particle swarm optimization fruit fly optimization algorithm gray wolf optimizer

来源：评论

学校读者我要写书评

暂无评论

Optimized deep residual network system for diagnosing tomato pests

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 195卷 106805-106805页

作者： Yu, Helong Liu, Jiawen Chen, Chengcheng Heidari, Ali Asghar Zhang, Qian Chen, Huiling Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Univ Technol Wenzhou 325035 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Tomato production is often threatened by the bites of several pests (mainly whiteflies and cotton bollworms). Pests exist throughout the tomato growing season, and it is necessary to detect and prevent these stubborn pests in time to reduce the economic losses caused by pests. Deep learning has been widely used to identify plant diseases and insect pests in recent years. The performance of the deep learning model is greatly affected by the network structure and hyperparameters, especially for hyperparameters, which often require manual participation in selection. To obtain suitable hyperparameters, this paper improves the fruit fly optimization algorithm and uses the improved algorithm to optimize the learning rate of the deep network. The experimental results show that the improved fruit fly optimization algorithm can search for a better learning rate. Among the 6 networks participating in the experiment, IResNet50 has an average diagnostic accuracy of 94.4% for seven tomato pests, which is better than other models.

关键词： Tomato pests diagnosis Deep residual networks fruit fly optimization algorithm Classification

来源：评论

学校读者我要写书评

暂无评论

The optimized evidence k-Nearest Neighbor based on FOA under the hesitant fuzzy environment and its application in classification

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期39卷 1119-1129页

作者： Song, Chenyang Xu, Zeshui Zhang, Yixin Army Engn Univ PLA Command & Control Engn Coll Nanjing Peoples R China Sichuan Univ Business Sch Chengdu Peoples R China

The k-Nearest Neighbor (k-NN) is one of the simplest intelligent algorithms in the field of pattern recognition and classification. The increasing complexity of practical applications brings more uncertainty and fuzziness. In this paper, we take advantage of the Dempster-Shafer evidence theory (D-S evidence theory) and the hesitant fuzzy set (HFS) in depicting uncertain preference and information, and develop the evidence k-Nearest Neighbor (Ek-NN) under the hesitant fuzzy environment. The fruit fly optimization algorithm (FOA) is adopted to determine the most appropriate value of k in Ek-NN, and a specific implementation process of the optimized Ek-NN based on FOA is also provided. Moreover, two numerical examples about classification problems are presented to evaluate the performance of the proposed method. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized Ek-NN based on FOA under the hesitant fuzzy environment.

关键词： k-Nearest neighbor dempster-shafer evidence theory hesitant fuzzy set fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Robust aerodynamic shape optimization using a novel multi-objective evolutionary algorithm coupled with surrogate model

引用

STRUCTURAL AND MULTIDISCIPLINARY optimization 2020年第4期62卷 1969-1987页

作者： Tian, Xu Li, Jie Northwestern Polytech Univ Sch Aeronaut Dept Fluid Mech Xian 710072 Peoples R China

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm that has exhibited enormous values in engineering optimization. In this study, an effective multi-objective fruit fly optimization algorithm (MOFOA) incorporated with the Pareto dominance is investigated and applied to robust aerodynamic shape optimization considering uncertainties in the design process. In the MOFOA, the fruit flies are required to perform solution explorations within an adaptive search scope by flying around the Pareto non-dominated solutions. An enhanced clustering evolution mechanism of combining the cooperative local search and differential evolution operator is elaborately designed to enrich the exploratory capacities for complex optimization. Paired with the search strategy, the non-dominated sorting technique considering the quality as well as uniform distribution of Pareto solutions is adopted to deal with the multiple objectives. To generate high-quality and uniformly spread-out Pareto non-dominated solutions, the roulette wheel selection (RWS) operator based on crowding distance is employed to guide the selection processes of individuals throughout various stages of the MOFOA. The effectiveness and superiority of MOFOA are demonstrated by the comparative studies on several benchmark functions. Finally, a robust aerodynamic design system for combining the MOFOA optimizer with a Kriging-based surrogate model and full Navier-Stokes computation is advantageously implemented to perform shape optimization on a transonic airfoil using the Taguchi robust design methodology that emphasizes the inherent variability while improving engineering productivity. The design results show that the robust design, compared with the single-point design, is capable of producing a set of robust solutions that are significantly less sensitive to input variations by capturing the Pareto-optimal front with respect to the criteria of aerodynamic performance and its stability.

关键词： fruit fly optimization algorithm Multi-objective optimization Cooperative local search Differential evolution operator Pareto-optimal solutions Robust aerodynamic shape optimization

来源：评论

学校读者我要写书评

暂无评论

Analysis and optimization of Cache-Enabled Fog Radio Access Networks: Successful Transmission Probability, Fractional Offloaded Traffic and Delay

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第5期69卷 5219-5231页

作者： Jiang, Yanxiang Peng, Aoao Wan, Chaoyi Cui, Ying You, Xiaohu Zheng, Fu-Chun Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Considering that fog access points (F-APs) can offer cooperation gain by jointly transmitting the same file or content diversity gain by concurrently transmitting the coded subfiles, we propose to use both joint transmission (JT) strategy and parallel transmission (PT) strategy to serve users. Using tools from stochastic geometry, we first derive the expressions of successful transmission probability (STP) under different transmission strategies, and then derive the expressions of fractional offloaded traffic (FOT) and delay. We further formulate the corresponding edge caching optimization problem to maximize the STP, FOT or minimize the delay. Considering the NP-hard property of the above optimization problem, we propose an improved fruit fly optimization (FFO) based edge caching scheme, which utilizes the dynamic search step to avoid pre-mature convergence. Furthermore, we theoretically analyze the convergence and global optimality of our proposed scheme. Numerical results show that a significant performance gain is achieved by using our proposed edge caching scheme compared with the baseline schemes.

关键词： Fog radio access networks edge caching stochastic geometry successful transmission probability fractional offloaded traffic delay fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm

引用

APPLIED INTELLIGENCE 2020年第9期50卷 2800-2817页

作者： Ge, Fawei Li, Kun Han, Ying Xu, Wensu Wang, Yi'an Bohai Univ Coll Engn Jinzhou 121013 Liaoning Peoples R China

In recent years, uninhabited aerial vehicles (UAV) have been used for oilfield inspections in many enterprises which can realize oilfield inspections by fewer workers. Path planning is one indispensable element in oilfield inspections through UAVs and it is also a complicated optimal problem. Now, although many researches have been focused on it, they are mainly discussed based on two-dimension planes. In practices, oilfields are complex three-dimensional spaces with many targeted points and moving obstacles between the starting and the ending point, which bring current methods some difficulties. In order to solve this problem, a three-dimensional environment model for oilfields is established for the first time, which includes: a static oil-well equipment, moving obstacles, and so on. Then, a cost function is defined to evaluate the best path, which includes: total length, average height, total time, and total electricity consumption. Finally, an improved pigeon-inspired optimization algorithm is proposed to solve problems about path planning in a three-dimensional dynamic environment of oilfields, which is named PIOFOA. In the PIOFOA, a pigeon-inspired optimization (PIO) algorithm is used to optimize the initial path and a fruit fly optimization algorithm (FOA) is used to continue local optimizations, so as to search the best path after movements of obstacles. Compared with some other methods, simulation results show that the proposed PIOFOA method is more effective.

关键词： Oilfield inspection Path planning UAV Pigeon-inspired optimization fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Two-Stage optimization Model of Agricultural Product Distribution in Remote Rural Areas

引用

IEEE ACCESS 2020年 8卷 213928-213949页

作者： Zhang, Hao Feng, Huixia Wang, Hongmei Beijing Technol & Business Univ Sch E Business & Logist Beijing 100048 Peoples R China Beijing Jiaotong Univ Beijing 100044 Peoples R China Beijing Food Safety Res Base Beijing 100044 Peoples R China

Due to the lack of systematically optimized logistics networks in many remote rural areas, the online sales of agricultural products in these areas have the disadvantages of high cost, high damage and slow speed. To address these logistic problems, this paper proposes a two-stage layout optimization model of agricultural product joint distribution centres based on the geographical features of remote rural areas. The number and location of distribution centres are selected in two stages to optimize the logistics network. Chengkou County, located in Chongqing city, China, has been selected as the study area. In the first stage, AP clustering is carried out using the distance between the logistics nodes of villages and towns, and the transit station of the source of agricultural products is obtained using the correlation between the nodes, which are regarded as alternative locations for the second-level logistics nodes. In the second stage, a joint distribution centre optimization model with the lowest cost is constructed. A fruit fly optimization algorithm is used to select the optimal locations from the alternative locations as the second-level logistics nodes and obtain the specific delivery path. Optimizing the logistics network of remote rural areas can reduce the logistics costs of agricultural products in these areas, promote online sales of agricultural products, and provide the government and logistics companies with theoretical references to open up new markets in remote areas.

关键词： Logistics Agricultural products optimization Mathematical model Heuristic algorithms Transportation Routing AP clustering fruit fly optimization algorithm joint distribution centres two-stage logistics layout 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：