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

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

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Soft sensor for the moisture content of crude oil based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第4期42卷 770-785页

作者： Li, Kun Xu, Wensu Han, Ying Ge, Fawei Wang, Yi'an Bohai Univ Coll Engn 19 Keji Rd Jinzhou 121013 Liaoning Peoples R China

In the practical oilfield production, it has great significance to realize timely and accurate measurement of the moisture content of crude oil. However, there are some drawbacks in the traditional measurement methods, such as: non-real time, high cost, labor-consume, vulnerability to environmental impacts, and so on. In order to solve these problems, a soft sensor model based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm (APFOA-MKGPR) is presented in this paper. A multiple kernels-based Gaussian process regression method is utilized to deal with the practical production process characterised by multiple operating phases, noises, strong nonlinearity and dynamic. In the multi-kernel function, many parameters (five hyper-parameters in the multi-kernel function and three weights of each kernel function) need to be accurately given, which is difficult to be effectively optimized by the maximum likelihood estimation. So, a swarm intelligence-based adaptive variable population fruit fly optimization algorithm (APFOA) is proposed to train the best model parameters. A novel adaptive variable population mechanism is developed to adaptively adjust the population size and the random flight distance during the iterations, which can realize a combination of the global searching and the local searching for the optimal solutions. The proposed method is verified by four benchmark functions and the actual production data of one oil well, and experimental results show the effectiveness for accurate prediction of the moisture content of crude oil.

关键词： Moisture content of crude oil soft sensor Gaussian process regression Multiple kernels fruit fly optimization algorithm

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A novel elitist fruit fly optimization algorithm

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SOFT COMPUTING 2023年第8期27卷 4823-4851页

作者： He, Jieguang Peng, Zhiping Qiu, Jinbo Cui, Delong Li, Qirui Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming Peoples R China Guangdong Univ Petrochem Technol Coll Elect Informat Engn Maoming Peoples R China

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed.

关键词： Swarm intelligence algorithm fruit fly optimization algorithm Elite guidance Boundary information Population diversity

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Twin support vector machines based on fruit fly optimization algorithm

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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

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Finding Community Modules for Brain Networks Combined Uniform Design with fruit fly optimization algorithm

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2020年第2期12卷 178-192页

作者： Zhang, Jie Feng, Junhong Yang, Yifang Wang, Jian-Hong Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Guangxi Peoples R China Xian Shiyou Univ Coll Sci Xian 710065 Shaanxi Peoples R China

There are a huge amount of neural units in brain networks. Some of the neural units have tight connection and form neural unit modules. These unit modules are helpful to the disease detection and target therapy. A good method can find neural unit modules accurately and effectively. The study proposes a new algorithm to analyze a brain network and obtain its neural unit modules. The proposed algorithm combines the uniform design and the fruit fly optimization algorithm (FOA);therefore, we called it as UFOA. It makes the utmost of their respective merits of the uniform design and the FOA, so as to acquire the feasible solutions scattered uniformly over the vector domain and find the optimal solution as quickly as possible. When compared with other existing methods, FOA and the uniform design are integrated first, and UFOA is first utilized to find unit modules from brain networks. 37 TD resting-state functional MRI brain networks are used to testify the performance of UFOA. The obtained experimental results manifest that UFOA is clearly superior to the other five methods in terms of modularity, and is comparable with the other five methods in terms of conductance. Additionally, the comparative analysis of UFOA and FOA also demonstrates that the uniform design brings benefit to the improvement of UFOA.

关键词： fruit fly optimization algorithm Uniform design Brain networks Neural unit modules

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

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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

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The Study on Keywords Frequency Composite Function of Public Opinion toward Macau's Gambling Industry: Using the fruit fly optimization algorithm

The Study on Keywords Frequency Composite Function of Public...

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International Conference on Engineering, Management Science and Innovation (ICEMSI)

作者： Wu, Shianghau Shi, Yongdong Macau Univ Sci & Technol Fac Management & Adm Macau Peoples R China

ISBN: (纸本)9781479935574

This study at first used the text mining method to analyze the keywords of the Chinese news reports related to Macau's gambling industry from June to September 2012. The study got 19 major keywords at the first step. In order to comprehend the influence of each keyword in each document, the study applied the fruit fly optimization algorithm to evaluate the Keywords Frequency Composite Function. The contributions of the study included the following two points: First, the study applied the text mining method to get the 19 major keywords and found the public opinion focus during the time frame. Second, the study found the fruit fly optimization algorithm as a useful tool to evaluate the Keywords Frequency Composite Function.

关键词： text mining gambling industry public opinion fruit fly optimization algorithm

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Kernel-based Fuzzy C-means Clustering Based on fruit fly optimization algorithm

Kernel-based Fuzzy C-means Clustering Based on Fruit Fly Opt...

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IEEE International Conference on Grey Systems and Intelligent Services (GSIS)

作者： Wang, Qiuping Zhang, Yiran Xiao, Yanting Li, Jidong Xian Univ Technol Sch Sci Xian Shaanxi Peoples R China Southeast Univ Coll Civil Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509066681

Fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems of both a local optimum and depending on initialization strongly in the fuzzy c-means clustering algorithm (FCM), a method of kernel-based fuzzy c-means clustering based on fruit fly algorithms (FOAKFCM) is proposed in this paper. In this algorithm, the fruit fly algorithm is used to optimize the initial clustering center firstly, kernel-based fuzzy c-means clustering algorithm (KFCM) is used to classify data. At the same time we reference classification evaluation index to choose the fuzziness parameter in adaptive way. The clustering performance of FCM algorithm, KFCM algorithm, and the proposed algorithm is testified by test datasets. FCM algorithm and FOAKFCM are used for power load characteristic data classification, respectively. Experiment results show that FOAKFCM algorithm proposed overcomes FCM's defects efficiently and improves the clustering performance greatly.

关键词： Clustering analysis fruit fly optimization algorithm Fuzzy c-means clustering algorithm KFCM Fuzzy degree of parameters

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A Grey Neural Network Model Optimized by fruit fly optimization algorithm for Short-term Traffic Forecasting

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ENGINEERING LETTERS 2017年第2期25卷 198-204页

作者： Pan, Yuanyuan Shi, Yongdong Hubei Univ Technol Sch Comp Sci Wuhan 430070 Peoples R China Macau Univ Sci & Technol Sch Business Taipa Macao Peoples R China

Accurate short-term traffic forecasting can relieve traffic congestion and improve the mobility of transportation, which is very important for management modernization of transportation systems. However, it is quite difficult to predict effectively and accurately short-term traffic information since traffic information shows the strongly nonlinear, complex and uncertain characteristics. In this paper, a new hybrid model based on grey neural network and fruit fly optimization algorithm (FOA) is proposed to solve this problem. The FOA is used to select the appropriate parameter values of the grey neural network model, thereby improving the accuracy of forecasting model. The proposed hybrid model can exploit sufficiently the characteristics of grey system model requiring less data, the non-linear map of neural networks and the quick-speed convergence of FOA, and has simpler structure. The effectiveness of this proposed hybrid model is proved by experiment simulation. The experiment results show that the proposed model has better performance than the single grey model GM(1,1), the single back-propagation neural network (BPNN) model, the combined model of them, i.e., the grey neural network (GNN) model, and the GNN model with particle swarm optimization (GNN-PSO), on short-term traffic forecasting.

关键词： Short-term traffic forecasting Grey system Neural networks Grey neural network fruit fly optimization algorithm

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

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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

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