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Two-step based feature selection method for filtering redundant information

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第4期33卷 2059-2073页

作者： Wang, Youwei Feng, Lizhou Li, Yang Cent Univ Finance & Econ Sch Informat Beijing 100081 Peoples R China Tianjin Univ Finance & Econ Sch Sci & Engn Tianjin Peoples R China

In text classification field, many classifiers cannot deal with the features with large dimensions, thus it is very important to filter the redundant information from the original feature space efficiently and achieve the features with best qualities. On this basis, a new two-step based feature selection method is proposed in this paper. Firstly, some definitions (word semantic correlation, set semantic correlation, semantic correlative and semantic correlative set) are introduced, and the algorithm of generating the semantic correlative sets is given. Secondly, the process of the two-step based feature selection method is described: in the first step, a feature subset is obtained by using an optimal feature selection method, and a set of semantic correlative sets is generated by using the selected feature subset;in the second step, the redundant information of the selected features is filtered by using the generated semantic correlative sets. Finally, in order to avoid local optimum when searching the best threshold, the conception of memory recall position is introduced and an improved memory recall mechanism based fruit fly optimization algorithm is proposed. In the experiments, two typical classifiers: support vector machine and naive bayes are used on four datasets: Reuters50, SMSSPAS, WebKB and 20-Newsgroups, and the 10-cross validation is carried out when the measurements of F-1 and receiver operating curve are used. Experimental results show that the proposed method achieves higher accuracy than several representative traditional feature selection methods and runs faster than typical mutual information based feature selection methods, illustrating its effectiveness on achieving the best features in text classification filed.

关键词： Feature selection redundant information feature space fruit fly optimization algorithm support vector machine receiver operating curve

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Optimizing pile bearing capacity prediction: Insights from dynamic testing and smart algorithms in geotechnical engineering

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MEASUREMENT 2024年 230卷

作者： Fattahi, Hadi Ghaedi, Hossein Malekmahmoodi, Farshad Armaghani, Danial Jahed Arak Univ Technol Fac Earth Sci Engn Arak Iran Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW 2007 Australia

In contemporary mining and geotechnical projects, various approaches are employed to predict the bearing capacity of piles (Qu). However, accurately modeling pile behavior using numerical, experimental, analytical, and regression methods proves challenging and, at times, infeasible due to the intricate nature of geotechnical materials, uncertainties, and the interaction between soil and piles. Consequently, formulations are generally presented, incorporating assumptions and simplifications that deviate from the actual complexity of the problem. Moreover, despite the high accuracy of pile loading tests as a reliable method in various design stages, their high costs and time requirements deter designers from conducting field tests. To address these challenges, this study performed 50 dynamic tests (HSDT) on precast concrete piles in Indonesia, Pekanbaru, to generate the necessary datasets. To mitigate experimental costs, two optimization algorithms fruit fly optimization (FFO) and invasive weed optimization (IWO) were employed. These models incorporated pile set (S), drop height (H), hammer weight (W), cross-sectional area (A), and length (L) as input parameters. Finally, the models' accuracy was evaluated using squared correlation coefficient (R2), variance accounted for (VAF), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results revealed that the FFO algorithm achieved an accuracy range of 0.971-0.978. Similarly, the IWO algorithm exhibited an accuracy range of 0.984-0.988. Additionally, sensitivity analysis indicated that, among the input parameters, W had the most significant impact on the Qu in both algorithms.

关键词： Pile bearing capacity fruit fly optimization algorithm Invasive weed optimization algorithm Sensitivity analysis Dynamic tests

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Free-form surface parts quality inspection optimization with a novel sampling method

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APPLIED SOFT COMPUTING 2018年 62卷 550-570页

作者： Li, Taifeng Gao, Liang Pan, Quanke Li, Peigen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Quality inspection is a critical step in the process of manufacturing of free-form surface parts. For dimensional quality control, registration is normally used to match the measurement model to the design model. Then, the machined errors can be visualized in the misalignment regions. Because these two models are located in different coordinate systems, an optimal spatial transformation matrix is necessary. In this area, iterative closest point (ICP) is the best known method for registration. However, the global optimum solution is intractable to compute in terms of ICP. Considering of the accuracy and efficiency of free-form surface parts quality inspection, in this paper, the maximin distance approach is introduced for model simplification with normal deviation information. Moreover, to find the optimal transformation parameters, an improved fruit fly optimization algorithm is proposed in terms of the saccade behavior of drosophila. The performance of the developed algorithms has been tested via both computer simulations and real experiments, and the results demonstrate that the proposed methods are superior to several classical approaches for free-form surface parts quality inspection. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Quality inspection Registration Maximin distance method fruit fly optimization algorithm

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A Rolling Bearing Fault Diagnosis Method Based on Improved CEEMDAN and RCMFE

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第1期33卷

作者： Luo, Zhiyong Zhu, Guangming Dong, Xin Tan, Hongkai Li, Jialin Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing Peoples R China

Considering the problem of residual noise and spurious modes in the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a rolling element bearing malfunction diagnostic method based on improved CEEMDAN (ICEEMDAN) is proposed. First, different from the CEEMDAN, which directly adds Gaussian white noise with a mean of zero, the proposed method adds the kth component obtained from white noise decomposed by empirical mode decomposition (EMD) to the vibration signal, and then the ICEEMDAN is employed to decompose the signal into several intrinsic mode functions (IMFs). Second, aiming at the uncertainty problem of entropy estimation in multi-scale fuzzy entropy (MFE), a refined composite multi-scale fuzzy entropy (RCMFE) is proposed to obtain the characteristic from the selected IMFs. Finally, smoothing factor of PNN is determined by fruit fly optimization algorithm (FOA), and the extracted features are input into the FOA-PNN model to achieve condition identification. Experimental results illustrate that the identification accuracy is more than 99%, which indicates its high effectiveness and superiority.

关键词： Rolling bearing fault diagnosis empirical mode decomposition multi-scale fuzzy entropy fruit fly optimization algorithm probabilistic neural network

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A hybrid model for monthly water demand prediction: A case study of Austin, Texas

AWWA WATER SCIENCE

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AWWA WATER SCIENCE 2020年第2期2卷

作者： Lu, Hongfang Matthews, John Han, Shuai Louisiana Tech Univ Trenchless Technol Ctr Ruston LA USA Louisiana Tech Univ Trenchless Technol Ctr 599 Dan Reneau Dr Ruston LA 71270 USA

Water demand prediction is an essential prerequisite for urban water resource allocation and pipeline network management. This article uses a hybrid model to improve the accuracy of predicting water demand. The model combines a simulated annealing (SA) algorithm, genetic cross factor (GCF), fruit fly optimization algorithm (FFOA), and support vector machine (SVM) to predict monthly water demand. The monthly water consumption data from 2012 to 2017 in Austin, Texas, are used to develop and test the model. The prediction results are compared with other intelligent algorithms, such as the hybrid model combination particle swarm optimizer with SVM and back-propagation neural network. The results show that the mean absolute percentage error of FFOA-GCFSA-SVM is the smallest, and it is reduced by 16% and 51.17%, respectively, compared with the other two models. Moreover, the influences of kernel function and input variables on prediction results are analyzed, and the effects of sliding window length, data classification, and forecasting period on prediction results are discussed. We concluded that (1) for the city of Austin, the model with a Gaussian kernel function and historical data period of 6 months has higher prediction accuracy;(2) the monthly water demand in Austin is mainly influenced by the local population;and (3) data classification by season can reduce the interference of temperature on prediction.

关键词： fruit fly optimization algorithm genetic cross factor monthly simulated annealing algorithm support vector machine water demand prediction

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The error prediction of inventory reconciliation during storage and transportation process based on PLS and MFOA-LSSVM

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2017年第3期7卷 895-904页

作者： Shui, Aishe Fang, Hanming Zong, Fuxing Wu, Yuheng Wang, Fei Logist Engn Univ Dept Logist Informat & Logist Engn Chongqing Peoples R China Logist Engn Univ Dept Management Sci & Engn Chongqing Peoples R China

Process optimization and fault diagnosis technology, which is represented by production process monitoring, design and production condition adjustment, play an important role in the modern petroleum industry. Accurate inventory reconciliation model is the basis of process optimization and fault diagnosis. To eliminate the impact of the inventory reconciliation error caused by different metering systems, the error prediction method of inventory reconciliation during storage and transportation process based on partial least squares (PLS) and least squares support vector machine optimized by modified fruit fly optimization algorithm (MFOA-LSSVM) is proposed. The general error prediction method flow of inventory reconciliation is provided. The principles of PLS and MFOA-LSSVM are elaborated in detail. Firstly, the algorithm of PLS is used to exclude the interference of unrelated factors and extract the most relevant factors that influence the error of inventory reconciliation. Then the modified three-dimensional fruit fly optimization algorithm with diminishing steps as well as a good global search capability is adopted to select the LSSVM model parameters and build the error prediction model. Finally, the sample data were revised by using the predictive value to verify the validity of the proposed method. The experimental modeling was carried out by PLS and MFOA-LSSVM. Compared with other forecasting methods, this method not only has the advantage of faster calculations, but also can well predict the error of reconciliation.

关键词： Inventory reconciliation Error prediction Partial least squares Least squares support vector machine fruit fly optimization algorithm

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A dynamic SVR-ARMA model with improved fruit fly algorithm for the nonlinear fiber stretching process

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NATURAL COMPUTING 2019年第4期18卷 747-756页

作者： Guo, Fan Ren, Lihong Jin, Yaochu Ding, Yongsheng Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Univ Surrey Dept Comp Sci Guildford GU27XH Surrey England

The fiber stretching process plays the key role in the process of fiber production and its effects is measured by the stretching ratio. The stretching ratio is determined by the relative speed of the winding roller. The stretching ratio has impact on the performance of the final fiber filament and production directly. Focused on the importance of the stretching ratio, the support vector regression (SVR) predictive model, called nonlinear auto-regressive exogenous model, for the fiber stretching rate based on existing industry data is proposed. Furthermore, the fruit fly optimization algorithm inspired by immune mechanism and cooperation functional (IFOA) is presented, and then is used to optimize the parameters in SVR. Furthermore, taking into account the high cost and accurate precision of the fiber stretching process, a time series autoregressive moving average (ARMA) model is introduced to reduce the prediction error of the IFOA-SVR model. Simulations results demonstrate that the proposed IFOA-SVR method can increase the prediction accuracy than the traditional FOA and the SVR method, and the ARMA model is essential to modify the prediction error of the IFOA-SVR model.

关键词： SVR model ARMA model fruit fly optimization algorithm Fiber stretching process Prediction

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A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments

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APPLIED INTELLIGENCE 2019年第7期49卷 2603-2622页

作者： Tang, Hongwei Sun, Wei Yu, Hongshan Lin, Anping Xue, Min Song, Yuxue Hunan Univ State Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Shaoyang Univ Hunan Prov Key Lab Grids Operat & Control Multipo Shaoyang 422000 Peoples R China Hunan Univ Hunan Prov Key Lab Intelligent Robot Technol Elec Changsha 410082 Hunan Peoples R China

In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm optimization (PSO) and fruit fly optimization algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this paper presents a novel multi-swarm hybrid FOA-PSO (MFPSO) algorithm for robot target searching. The main contributions of the proposed method are as follows. (1) The improved FOA (IFOA) provides a better value for the improved PSO (IPSO) to find the next optimal robot position value. (2) Multi-swarm strategy is introduced to enhance the diversity and achieve an effective exploration to avoid premature convergence and falling into local optima. (3) An escape mechanism named MSCM (Multi-Scale Cooperative Mutation) is used to address the limitation of local optima and enhance the escape ability for obstacle avoidance. All of the aspects mentioned above lead robots to the target without falling into local optima and allow the search mission to be performed more quickly. Several experiments in four parts are performed to verify the better performance of MFPSO. The experimental results show that the performance of MFPSO is much more significant than that of other current approaches.

关键词： Particle swarm optimization fruit fly optimization algorithm Target searching Multi-swarm Multi-scale cooperative mutation

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Opportunistic maintenance for wind turbines considering imperfect, reliability-based maintenance

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RENEWABLE ENERGY 2017年 103卷 606-612页

作者： Zhang, Chen Gao, Wei Guo, Sheng Li, Youliang Yang, Tao Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

Corrective maintenance and time-based maintenance have been widely employed in wind farms. However, the actual maintenance strategy is much more complex and diverse, and also maintenance actions do not always return the system to "as good as new" condition. Moreover, dependencies exist in multi-component systems, and it is more economical to perform preventive maintenance whenever a repair opportunity presents itself. For an opportunistic maintenance strategy, when the system is shut down for maintenance, the maintenance time of other still functioning parts can be moved up to take advantage of the huge downtime losses. In this paper, an opportunistic maintenance approach is proposed for wind turbines considering an imperfect maintenance schedule that is based on reliability. A hybrid hazard rate model is used to describe imperfect maintenance and explain the effects of corrective maintenance action. Comparative examples are used to illustrate the economic advantages of opportunistic maintenance. Simulation results demonstrate the impact that various maintenance costs (fixed downtime cost and preventive replacement cost) have on the economic benefit of opportunistic maintenance strategy. The simulation analysis also provides the optimal opportunistic maintenance reliability threshold using the fruit fly optimization algorithm to provide guidance for the actual operation and maintenance of wind farms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wind turbine Opportunistic maintenance Reliability theory Imperfect maintenance fruit fly optimization algorithm

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State prediction of hydro-turbine based on WOA-RF-Adaboost

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ENERGY REPORTS 2022年 8卷 13129-13137页

作者： Lan, Chaofeng Song, Bowen Zhang, Lei Fu, Lirong Guo, Xiaoxia Sun, Chao Harbin Univ Sci & Technol Sch Measurement & Commun Engn Harbin Peoples R China Beidahuang Ind Grp Gen Hosp Harbin 150088 Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Hainan Peoples R China

Aiming at the problem that the prediction performance of remote operation state of hydro-turbine needs to be improved, this paper uses whale optimization algorithm (WOA) to optimize random forest (RF). It combines it with Adaboost algorithm to propose the prediction model in this paper. Firstly, the signal of hydro-turbine is analyzed by variational modal decomposition (VMD), and the penalty factor and the number of IMF components K of VMD are optimized by fruit fly optimization algorithm (FOA). The arrangement entropy of intrinsic mode function (IMF) components, kurtosis, mean value of original signal are calculated, and the input eigenvector of hydro-turbine state prediction model is constructed;Secondly, the number of split attribute sets and the optimal number of decision trees of RF are optimized by WOA, and multiple WOA-RF models are iteratively trained by Adaboost algorithm to construct WOA-RF-Adaboost state prediction model. The prediction effect of the proposed model and the traditional model is evaluated by correct rate and confusion matrix. The results show that the prediction accuracy of WOA-RF-Adaboost model proposed in this paper is 99.2%, it has good state prediction performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Hydro-turbine State prediction Variational modal decomposition fruit fly optimization algorithm Whale optimization algorithm Random forest

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