检索结果-内蒙古大学图书馆

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

An effective and efficient fruit fly optimization algorithm with level probability policy and its applications

引用

KNOWLEDGE-BASED SYSTEMS 2016年 97卷 158-174页

作者： Wang, Lin Liu, Rui Liu, Shan Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China

An improved fruit fly optimization algorithm (FOA) is proposed for optimizing continuous function problems and handling joint replenishment problems (JRPs). In the proposed FOA, a level probability policy and a new mutation parameter are developed to balance the population diversity and stability. Twenty-nine complex continuous benchmark functions are used to verify the performance of the FOA with level probability policy (LP-FOA). Numerical results show that the proposed LP-FOA outperforms two state-of-the-art variants of FOA, the differential evolution algorithm and particle swarm optimization algorithm, in terms of the median and standard deviations. The LP-FOA with a new and delicate coding style is also used to handle the classic JRP, which is a complex combinatorial optimization problem. Experimental results reveal that LP-FOA is better than the current best intelligent algorithm, particularly for large-scale JRPs. Thus, the proposed LP-FOA is a potential tool for various complex optimization problems. (C) 2016 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Level probability Continuous function problem Joint replenishment problems

来源：评论

学校读者我要写书评

暂无评论

Constructing ZSCORE-based financial crisis warning models using fruit fly optimization algorithm and general regression neural network

引用

KYBERNETES 2016年第4期45卷 650-665页

作者： Huang, Tsui-Hua Leu, Yungho Pan, Wen-Tsao Natl Taiwan Univ Sci & Technol Grad Inst Management Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan

Purpose - In order to avoid enterprise crisis and cause the domino effect, which influences the investment return of investors, the national economy, and financial crisis, establishing a complete set of feasible financial early warning model can help to prevent the possibility of enterprise crisis in advance, and thus, reduce the influence on society and the economy. The purpose of this paper is to develop an efficient financial crisis warning model. Design/methodology/approach - First, the fruit fly optimization algorithm (FOA) is used to adjust the coefficients of the parameters in the ZSCORE model (we call it the FOA_ZSCORE model), and the difference between the forecasted value and the real target value is calculated. Afterward, the generalized regressive neural network (GRNN model), with optimized spread by FOA (we call it FOA_GRNN model), is used to forecast the difference to promote the forecasting accuracy. Various models, including ZSCORE, FOA_ZSCORE, FOA_ZSCORE+GRNN, and FOA_ZSCORE+FOA_GRNN, are trained and tested. Finally, different models are compared based on their prediction accuracies and ROC curves. Furthermore, more appropriate parameters, which are different from the parameters in the original ZSCORE model, are selected by using the multivariate adaptive regression splines (MARS) method. Findings - The hybrid model of the FOA_ZSCORE together with the FOA_GRNN offers the highest prediction accuracy, compared to other models;the MARS can be used to select more appropriate parameters to further improve the performance of the prediction models. Originality/value - This paper proposes a hybrid model, FOA_ZSCORE+FOA_GRNN which offers better performance than the original ZSCORE model.

关键词： fruit fly optimization algorithm Generalized regressive neutral network ROC curve Financial crisis forecast model

来源：评论

学校读者我要写书评

暂无评论

An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第1期9卷 80-90页

作者： Lei Wu Xiao Wensheng Liang Zhang Qi Liu Wang Jingli China Univ Petr Coll Mech & Elect Engn 66 Changjiang West Rd Qingdao Shangdong Provi Peoples R China

As a novel global optimization algorithm, the fruit fly optimization algorithm FOA has been successfully applied in a variety of mathematic and engineering fields. For the purpose of accelerating the convergence speed and overcoming the shortcomings of FOA, an improved fruit fly optimization called SEDI-FOA was proposed in this paper. In the proposed SEDI-FOA, more fruit flies would fly in the search direction that was best for finding the optimal solution, or at least in a direction close to the optimal direction. Experiments were conducted on a set of 12 benchmark functions, and the results showed that SEDI-FOA performed better than other several improved FOA and frequently-used intelligence algorithms, especially in the areas of accelerating convergence and global search ability and efficiency.

关键词： fruit fly optimization algorithm intelligent selection the best search direction benchmark functions experimental simulation

来源：评论

学校读者我要写书评

暂无评论

Twin support vector machines based on fruit fly optimization algorithm

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2016年第2期7卷 193-203页

作者： Ding, Shifei Zhang, Xiekai Yu, Junzhao China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou Peoples R China

Twin support vector machine (TWSVM), which solves classification problems through constructing two nonparallel planes by solving two related SVM-type problems, has become a hot spot in the field of machine learning. In addition to keeping the advantages of SVM, the classification performance of TWSVM is also significantly better than that of SVM. However, there are at least two parameters in TWSVM which need to specify. These parameters make great difference to the classification result of TWSVM and to find the optimal parameters is an important but difficult work. In order to overcome this deficiency, in this paper, we propose the twin support vector machines based on fruit fly optimization algorithm (FOA-TWSVM). This algorithm uses FOA-TWSVM, which has high optimization ability and small computation complexity, to select the parameters for TWSVM. The experimental results show that this algorithm is able to find the suitable parameters, and has higher classification accuracy compared with some other algorithms.

关键词： Twin support vector machine fruit fly optimization algorithm Pattern classification Parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Community Detection Utilizing a Novel Multi-swarm fruit fly optimization algorithm with Hill-Climbing Strategy

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2016年第3期41卷 807-828页

作者： Liu, Qiang Zhou, Bin Li, Shudong Li, Ai-ping Zou, Peng Jia, Yan Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Shandong Inst Business & Technol Coll Math & Informat Sci Yantai 264005 Shandong Peoples R China

The community detection methods based on evolutionary algorithm have become a hot research topic in recent years. However, most contemporary evolution-based community detection algorithms need many parameters in the initialization process and are characterized by complicated computational processes, which are puzzled for users to have a better understanding of these parameters on the performance of corresponding algorithm. In this paper, we first propose a new community detection method utilizing multi-swarm fruit fly optimization algorithm (CDMFOA), which needs only a few parameters and has a simple computational process. Moreover, we adopt the multi-swarm fruit fly strategy and hill-climbing method in community detection algorithm in order to resolve the premature convergence and improve the local search ability of CDMFOA. Meanwhile, we separately utilize modularity and modularity density as objective function in the framework of the CDMFOA, named CDMFOA_Q and CDMFOA_D, so as to check their detection abilities and accuracies in partitioning communities of complex networks. The experimental results on synthetic and real-world networks show that CDMFOA can effectively detect community structure in complex networks. Besides, we also demonstrate that the CDMFOA_D performs better than CDMFOA_Q and other traditional modularity-based methods.

关键词： Complex networks Community detection Community structure fruit fly optimization algorithm Evolutionary algorithm Modularity Modularity density

来源：评论

学校读者我要写书评

暂无评论

optimization Design for Screw Wash-Sand Machine Based on fruit fly optimization algorithm

引用

JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2016年第2期19卷 149-161页

作者： Fu, Yun-Fei Gong, Jie Peng, Zheng Li, Ji-Hua Li, Si-Dong Li, Pu-Wang Yang, Zi-Ming Chinese Acad Trop Agr Sci Agr Prod Proc Res Inst Chinese Agr Minist Key Lab Trop Crop Prod Proc Zhanjiang 524001 Peoples R China

The aim of this study is to minimize the specific energy consumption of the screw wash-sand machine. Let the diameter of the screw structure, pitch, diameter of the screw axis, blade thickness, installation angle, and the speed of the screw axis be the design variables, and take the minimum specific energy consumption as the optimization objective. According to the complexity of the optimization problem in this study, the fruit fly optimization algorithm (FOA) is used to execute the optimization design of the screw wash-sand machine. The non-stationary multi-stage assignment penalty function is adopted to cope with the constrained optimization problem. To judge the stability and reliability of the optimal solution and find the sensitive factors of the optimization design, the sensitivity analyses of the objective function and constraint conditions to the design variables are carried out. By simulation, the optimized structure parameters of the screw wash-sand machine and the data of the objective sensitivity and constraint sensitivity are obtained. The simulation results show that the specific energy consumption decreases by 4.59%;the diameters of the screw structure and screw axis are sensitive factors of the optimization design.

关键词： fruit fly optimization algorithm Non-stationary Multi-stage Assignment Penalty Function Wash-sand Machine Sensitivity Analysis Specific Energy Consumption

来源：评论

学校读者我要写书评

暂无评论

A New Improved fruit fly optimization algorithm for Traveling Salesman Problem 8

A New Improved Fruit Fly Optimization Algorithm for Travelin...

引用

8th International Conference on Advanced Computational Intelligence (ICACI)

作者： Yin, Lvjiang Li, Xinyu Gao, Liang Lu, Chao Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

ISBN: (纸本)9781467377829

Traveling salesman problem (TSP) which is a classic combinational optimization problem has a wide range of applications in many areas. Many researchers focus on this problem and propose several algorithms. However, it was proved to be NP-hard, which is very difficult to be solved. No algorithm can solve any types of this problem effectively. In order to propose an effective algorithm for TSP, this paper improves the fruit fly optimization algorithm (FOA) proposed recently. As far as we know, the FOA has not yet been applied to solve TSP. Therefore, several modifications of FOA have to be made to meet the characteristics of TSP. Based on the whole search framework and the essence of FOA, some operations of particle swarm optimization (PSO) have been introduced into this method. In the smell search phase, the cluster mechanism of the fruit flies has been used to copy flies to one point and the mutation operation of genetic algorithm is used as the method of information exchanging among fruit flies for random search. In the visual search phase, the generalized PSO is applied to balance the global search and local search abilities of proposed algorithm. To evaluate the performance of proposed algorithm, some experiments and comparisons with other reported algorithms have been conducted. The results show the feasibility and effectiveness of proposed algorithm in solving TSP.

关键词： fruit fly optimization algorithm evolutionary algorithms meta-heuristics traveling salesman problem

来源：评论

学校读者我要写书评

暂无评论

Image Restoration Based on Structure and fruit fly optimization algorithm 7

Image Restoration Based on Structure and Fruit Fly Optimizat...

引用

2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Yibo Wang Yanping Bai Yan Hao School of science North University of China

ISBN: (纸本)9781467399050

Image restoration is one of the hot spots in the image vision *** algorithm is a commonly used *** order to avoid the problem of inaccurate priority calculation caused by confidence dropping rapidly,and considering the variance difference between patches can reflect the image edge structure,we redefine the priority *** fruit fly optimization algorithm is applied to the search process of the best matching patch,which avoids the heavy workload and inaccuracy caused by the global search,and it improves the efficiency and accuracy of the *** restoring the different damaged images,it can be seen that not only the PSNR values are improved,but also we can get a better restoration result.

关键词： component Criminisi algorithm the priority variance difference the best matching patch fruit fly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Multi-class Classification Approach Based on fruit fly optimization algorithm and Relevance Vector Machine 8

A Novel Multi-class Classification Approach Based on Fruit F...

引用

8th International Conference on Intelligent Networking and Collaborative Systems (INCoS)

作者： Kang, Jianshe Wu, Kun Chi, Kuo Wang, Xuan Mech Engn Coll Dept Equipment Command & Management Shijiazhuang Peoples R China China Armed Police Force Engn Univ Dept Elect Technol Xian Peoples R China

ISBN: (纸本)9781509041237

A novel version of multi-class classification method based on fruit fly optimization algorithm (FOA) and relevance vector machine (RVM) is proposed. The one-against-one-against-rest (OAOAR) classification model based on the traditional one-against-one (OAO) and one-against-rest (OAR) algorithm is aimed at combining the advantages of them and translates the multi-class classification problem into multiple three-classification problems to accelerate the running speed with high classification precision. With RVM applied as the binary classifier, the optimal parameter values of RVM kernel function are determined by FOA. Theoretical analysis and computational comparisons on publicly available datasets both indicate that the proposed approach outperforms in terms of diagnosis accuracy and running time with more model sparsity and higher diagnosis efficiency.

关键词： relevance vector machine fruit fly optimization algorithm multi-class classification one-against-one one-against-rest

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：