检索结果-内蒙古大学图书馆

Moving distance measurement for hydraulic support based on fruit fly optimization algorithm

OPTICAL ENGINEERING 2017年第1期56卷 1-10页

作者： Wang, Jiabiao Wang, Zhongbin Xu, Jing Tan, Chao Si, Lei China Univ Min & Technol Coll Mechatron Engn 1 Daxue Rd Xuzhou 221116 Peoples R China

Due to the inaccurate and unreliable moving distance measurement of the hydraulic support in mines, a method based on the random circle detection (RCD) algorithm and the fruit fly optimization algorithm (FOA) is proposed. According to the changing center and radium of the circle on the support, the relative position of adjacent supports is acquired by the camera. The noise of the collected image is moved, and the edge feature is protected using a bilateral filter. A local adaptive threshold algorithm is used for binary processing of the image. Then, RCD is used to detect the contour, which is similar to the circle. A method to detect the circle based on FOA is used to accurately detect the circle. Subsequently, the relative distance is calculated according to the change of the circle. Finally, the accuracy and reliability of the proposed method are verified though the experiment. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： distance measurement circle detection fruit fly optimization algorithm random circle detection algorithm hydraulic support

来源：评论

学校读者我要写书评

暂无评论

A new type bionic global optimization: Construction and application of modified fruit fly optimization algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2015年第9期229卷 1614-1621页

作者： Qi Qisong Xu Gening Fan Xiaoning Wang Jun Taiyuan Univ Sci & Technol Dept Mech Engn Taiyuan Shanxi Peoples R China

fruit fly optimization algorithm, which is put forward through research on the act of foraging and observing groups of fruit flies, has some merits such as simplified operation, strong robustness, easy to parallel computing, and fast convergence rate;it could solve the bottlenecks of traditional intelligent optimization algorithms on precocity and low convergence speed effectively. fruit fly optimization algorithm is applied to almost all the numerical optimization problems and is very useful in engineering applications. When the design variable is negative, traditional fruit fly optimization algorithm is not qualified for the extraordinarily slow convergence rate during the late stage of calculation and easy to be trapped in local optimum. Because of the defects of classical fruit fly optimization algorithm, a new coding method of the process of optimization is improved by this article, so the design variables could be searched toward the direction. In addition, a novel bionic global optimizationfruit fly optimization algorithm of learningis proposed by introducing the concept of study. This article tries to apply fruit fly optimization algorithm of learning to compare calculations;therefore, four classical test functions and two engineering problems are performed. It turned out that not only does fruit fly optimization algorithm of learning inherit the advantages of fruit fly optimization algorithm, but has a strong learning ability. The introduction of study ability into fruit fly optimization algorithm notably improves the efficiency and capability of optimization;it has characteristics of fast convergence rate and fast speed of approaching the global optimum solutions. fruit fly optimization algorithm of learning has the ability to solve practical problems, and its engineering prospect is promising.

关键词： fruit fly optimization algorithm numerical optimization fruit fly optimization algorithm of learning study

来源：评论

学校读者我要写书评

暂无评论

Bimodal fruit fly optimization algorithm based on cloud model learning

引用

SOFT COMPUTING 2017年第7期21卷 1877-1893页

作者： Wu, Lianghong Zuo, Cili Zhang, Hongqiang Liu, Zhaohua Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China

The fruit fly optimization algorithm (FOA) is one of the latest swarm intelligence-based methods inspired by the foraging behavior of fruit fly swarm. To improve the global search ability and solution accuracy of the FOA, a bimodal adaptive fruit fly optimization algorithm using normal cloud learning (BCMFOA) is proposed in this paper. Based on the labor allocation characteristics of the swarm foraging behavior, the fruit fly population is divided into two groups in the optimization process according to their duties of searching or capturing. The search group is mainly based on the fruit fly's olfactory sensors to find possible global optima in a large range, while the capture group makes use of their keen visions to exploit neighborhood of the current best food source found by the search group. Moreover, the randomness and fuzziness of the foraging behavior of fruit fly swarm during the olfactory phase are described by a normal cloud model. Using a normal cloud generator and an adaptive parameter updation strategy, the search range of the fruit fly population is adaptively adjusted. Therefore, the ability of FOA to avoid local optima is enhanced greatly. Twenty-three benchmark functions are used to test the performance of the proposed BCMFOA algorithm. Numerical results show that the proposed method can significantly improve the global search ability and solution accuracy of FOA. Compared with existing methods such as PSO, DE, AFAS, the experimental results indicate that BCMFOA has better or comparative convergence performance and accuracy. The application to the multi-parameter estimation of a permanent magnet synchronous motor further confirms its good performance.

关键词： fruit fly optimization algorithm Bimodal Normal cloud model Parameter adaptive

来源：评论

学校读者我要写书评

暂无评论

Optimum deployment of sensor nodes in wireless sensor network using hybrid fruit fly optimization algorithm and bat optimization algorithm for 3D Environment

引用

PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第6期15卷 2694-2718页

作者： Mohar, Satinder Singh Goyal, Sonia Kaur, Ranjit Punjabi Univ Dept Elect & Commun Engn Patiala Punjab India

Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influences the performance of wireless sensor network. For better performance of wireless sensor network it is essential to increase the coverage of nodes by locating the nodes at optimum positions with help of efficient optimization algorithm. In this paper the sensor nodes are located by using Hybrid fruit fly optimization algorithm and bat optimization algorithm in three dimensional environment. The exploration feature of fruit fly optimization algorithm is combined with exploitation characteristics of bat optimization algorithm in proposed algorithm. The grid points covered once by sensor node are removed from entire grid points for remaining nodes. With removal of grid points the overload on sensor nodes is reduced in proposed algorithm. The simulation results of Hybrid fruit fly optimization algorithm and bat optimization algorithm are compared in terms of variance, standard deviation, coverage rate and coefficient of dispersion with fruit fly optimization algorithm and bat optimization algorithm. Moreover, to verify the efficiency of proposed algorithm the results are also compared with other optimization algorithms such as artificial bee colony algorithm with dynamic search strategy, grey wolf optimization algorithm, enhanced grey wolf optimization algorithm, whale optimization algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm, differential evolution algorithm, shuffled frog leaping algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm based on differential evolution respectively. The simulation result signifies that proposed Hybrid fruit fly optimization algorithm and bat optimization algorithm is efficient than above stated existing optimization algorithm in terms of average coverage rate.

关键词： Sensor nodes Bat optimization algorithm Wireless sensor network Node deployment fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints

引用

EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 28-39页

作者： Zheng, Xiao-long Wang, Ling Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, an unrelated parallel machine scheduling problem with additional resource constraints (UPMSP_RC) from the real world manufacturing systems is studied. With the objective of minimizing the makespan, a mixed integer linear programming model is presented and several properties are analyzed. Furthermore, a two-stage adaptive fruit fly optimization algorithm (TAFOA) is proposed to solve the UPMSP_RC. At the first stage, a heuristic is proposed to generate an initial solution with high quality. At the second stage, the initial solution is adopted as the initial swarm center for further evolution. During the evolution, the search manners are selected adaptively with the guidance of the problem-specific knowledge, which is a sufficient condition of the best schedule under a given job-to-machine assignment. Moreover, the effect of parameters on the performance of the TAFOA is investigated by using the two factor analysis of variance (ANOVA). Finally, extensive numerical comparisons are carried out to show the effectiveness of the TAFOA in solving the UPMSP_RC. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Unrelated parallel machine scheduling Resource constraint Knowledge

来源：评论

学校读者我要写书评

暂无评论

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第12期236卷 6912-6922页

作者： Liao, Qihao Yin, Qin Xie, Luofeng Yin, Guofu Sichuan Univ Sch Mech Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

Establishing a robust thermal error model is crucial for thermal error compensation of machine tools. In this paper, an improved exponential model for thermal error modeling was proposed. First, the improved exponential model is established by combining the exponential model and the temperature-dependent model. Second, the improved fruit fly optimization algorithm is used to determine the parameters. Finally, two strategies are used to select the optimal sensor location from six potential ambient temperature sensors. The ambient temperature data is proved to be effective in improving the robustness of the model. The average root mean square error of the proposed model, the exponential model without ambient temperature, and the linear model are 1.18 mu m, 1.79 mu m, and 2.70 mu m, respectively. The modeling results show that the proposed model has high accuracy and strong robustness, which is suitable for workshop environment and variable conditions in thermal error modeling of machine tools.

关键词： Thermal error modeling spindle exponential model fruit fly optimization algorithm grinding machine

来源：评论

学校读者我要写书评

暂无评论

Optimal Chiller Loading for Energy Conservation Using an Improved fruit fly optimization algorithm

引用

ENERGIES 2020年第15期13卷 3760-3760页

作者： Qi, Min-Yong Li, Jun-Qing Han, Yu-Yan Dong, Jin-Xin Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

In the multi-chiller of the air conditioning system, the optimal chiller loading (OCL) is an important research topic. This research is to find the appropriate partial load ratio (PLR) for each chiller in order to minimize the total energy consumption of the multi-chiller under the system cooling load (CL) requirements. However, this optimization problem has not been well studied. In this paper, in order to solve the OCL problem, we propose an improved fruit fly optimization algorithm (IFOA). A linear generation mechanism is developed to uniformly generate candidate solutions, and a new dynamic search radius method is employed to balance the local and global search ability of IFOA. To empirically evaluate the performance of the proposed IFOA, a number of comparative experiments are conducted on three well-known cases. The experimental results show that IFOA found 14 optimal values (the optimal values among all algorithms) under a total of 17CLs in three cases, and the ratio of the optimal values found was 82.4%, which was the highest among all algorithms. In addition, the mean value of all objective functions of IFOA is smaller and the standard deviation is equal to or close to 0, which proves that the algorithm has high stability. It can be concluded that IFOA is an ideal method to solve the OCL problem.

关键词： optimal chiller loading energy conservation fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Hybrid fruit fly optimization algorithm for the Realistic Hybrid Flowshop Rescheduling Problem in Steelmaking Systems

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2016年第2期13卷 932-949页

作者： Li, Jun-Qing Pan, Quan-Ke Mao, Kun Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

In this study, we propose a hybrid fruit fly optimization algorithm (HFOA) to solve the hybrid flowshop rescheduling problem with flexible processing time in steelmaking casting systems. First, machine breakdown and processing variation disruptions are considered simultaneously in the rescheduling problem. Second, each solution is represented by a fruit fly with a well-designed solution representation. Third, two novel decoding heuristics considering the problem characteristics, which can significantly improve the solution quality, are developed. Several routing and scheduling neighborhood structures are proposed to balance the exploration and exploitation abilities. Finally, we propose an effective HFOA with well-designed smell and vision search procedures. In addition, an iterated greedy (IG) local search is embedded in the proposed algorithm to further enhance its exploitation ability. The proposed algorithm is tested on sets of instances generated from industrial data. Through comprehensive computational comparisons and statistical analyses, the performance of the proposed HFOA algorithm is favorably compared against several algorithms in terms of both solution quality and efficiency.

关键词： fruit fly optimization algorithm heuristic hybrid flowshop iterated greedy rescheduling

来源：评论

学校读者我要写书评

暂无评论

A cloud model based fruit fly optimization algorithm

引用

KNOWLEDGE-BASED SYSTEMS 2015年第Nov.期89卷 603-617页

作者： Wu, Lianghong Zuo, Cili Zhang, Hongqiang Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China

fruit fly optimization algorithm (FOA) is a new global optimization algorithm inspired by the foraging behavior of fruit fly swarm. However, similar to other swarm intelligence based algorithms, FOA also has its own disadvantages. To improve the convergence performance of FOA, a normal cloud model based FOA (CMFOA) is proposed in this paper. The randomness and fuzziness of the foraging behavior of fruit fly swarm in osphresis phase is described by the normal cloud model. Moreover, an adaptive parameter strategy for Entropy En in normal cloud model is adopted to improve the global search ability in the early stage and to improve the accuracy of solution in the last stage. 33 benchmark functions are used to test the effectiveness of the proposed method. Numerical results show that the proposed CMFOA can obtain better or competitive performance for most test functions compared with three improved FOAs in recent literatures and seven state-of-the-arts of intelligent optimization algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Cloud model Randomness Fuzziness

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Filter/Wrapper Feature Selection Approach Based on Improved fruit fly optimization algorithm and Chi-square Test for High Dimensional Microarray Data

引用

CURRENT BIOINFORMATICS 2021年第1期16卷 63-79页

作者： Yan, Chaokun Wu, Bin Ma, Jingjing Zhang, Ge Luo, Junwei Wang, Jianlin Luo, Huimin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China China Mobile Online Serv Co Ltd Hong Kong Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China

Microarray data is widely utilized for disease analysis and diagnosis. However, it is hard to process them directly and achieve high classification accuracy due to the intrinsic characteristics of high dimensionality and small size samples. As an important data preprocessing technique, feature selection is usually used to reduce the dimensionality of some datasets. Given the limitations of employing filter or wrapper approaches individually for feature selection, in the study, a novel hybrid filter-wrapper approach is proposed for high dimensional datasets. First, the Chi-square Test is utilized to filter out some irrelevant or redundant features. Next, an improved binary fruit fly optimization algorithm is conducted to further search the optimal feature subset without degrading the classification accuracy. Here, the KNN classifier with the 10-fold-CV is utilized to evaluate the classification accuracy. Extensive experimental results on six benchmark biomedical datasets show that our proposed approach can achieve superior performance compared with other state-of-the-art methods.

关键词： Feature selection fruit fly optimization algorithm Chi-square test levy flight gaussian mutation microarray data

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：