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Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony algorithm: A Case Study of Grid-Connected PV System Power Prediction

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017年第1期2017卷 1-14页

作者： Gao, Xiang-ming Yang, Shi-feng Pan, San-bo Anyang Normal Univ Sch Phys & Elect Engn Anyang 455000 Peoples R China Tianjin Univ Sci & Technol Coll Elect Informat & Automat Tianjin 300222 Peoples R China

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMDand ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

关键词： SUPPORT vector machines bees algorithm PHOTOVOLTAIC power generation TIME series analysis PREDICTION models HILBERT-Huang transform CASE studies

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Using bees algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2011年第19期33卷 1747-1759页

作者： Behrang, M. A. Assareh, E. Assari, M. R. Ghanbarzadeh, A. Islamic Azad Univ Dept Mech Engn Dezful Branch Tehran Iran

In this study, an integrated multi-layer perceptron neural network and bees algorithm is presented for analyzing world CO2 emissions. For this purpose, the following steps are done: STEP 1: In the first step, the bees algorithm is applied in order to determine the world's fossil fuels and primary energy demand equations based on socio-economic indicators. The world's population, gross domestic product, oil trade movement, and natural gas trade movement are used as socio-economic indicators in this study. The following scenarios are designed for forecasting each socio-economic indicator in a future time domain: Scenario I: For each socio-economic indicator, several polynomial trend lines are fitted to the observed data and the best fitted polynomial (highest correlation coefficient (R-2) value) for each socio-economic indicator is used for future forecasting. Scenario II: For each socio-economic indicator, several neural networks are trained and the best trained network for each socio-economic indicator is used for future forecasting. STEP 2: In the second step, world CO2 emission is projected based on the oil, natural gas, coal, and primary energy consumption using bees algorithm. The related data from 1980 to 2006 are used, partly for installing the models (1980-1999) and partly for testing the models (2000-2006). World CO2 emission is forecasted up to year 2040.

关键词： artificial neural networks bees algorithm carbon dioxide emission forecasting fossil fuels primary energy

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Object-Based Distinction between Building Shadow and Water in High-Resolution Imagery Using Fuzzy-Rule Classification and Artificial Bee Colony Optimization

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JOURNAL OF SENSORS 2016年第7-11期2016卷 1-10页

作者： He, Yuanrong Zhang, Xinxin Hua, Lizhong Xiamen Univ Technol Coll Comp & Informat Engn Xiamen 361024 Peoples R China

Due to the high similarity of the spectra of urban water and building shadows, high-resolution satellite imagery often confuses and wrongly classifies these features. To address this problem, we propose an object-based method for distinguishing building shadow from water using an artificial bee colony algorithm. In the method, four spectral ratio bands are first calculated as additional input parameters for improving the accuracy of segmentation results. During the segmentation, a series of statistical factors, such as spectrum, ratio, and sharp features, are calculated to make up for defects in the high-resolution imagery. Finally, we propose a fuzzy-rule-based classifier to generate extraction rules. The classifier is based on artificial bee colony optimization, which employs the geometric mean (G-mean) as fitness function. The proposed method was carried out on two test sites in Xiamen City. The experimental results based on GF-1 satellite date show that, compared with SVM method, the proposed method improved the overall accuracy of extraction by approximately 6% to 15% and the kappa coefficient values by approximately 0.1 to 0.2. The analysis of the extraction rules also proves that the red/NIR band and the length-width ratio band are significantly influenced by the distinction between building shadow and water.

关键词： High resolution imaging bees algorithm Municipal water supply Shadow (Psychoanalysis) Cohen’s kappa coefficient (Statistics)

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Hybrid intelligent technique for automatic communication signals recognition using bees algorithm and MLP neural networks based on the efficient features

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 6000-6006页

作者： Shrme, Ataollah Ebrahimzadeh Babol Univ Technol Fac Elect & Comp Engn Babol Sar *** Iran

Automatic communication signal recognition plays an important role for many novel computer and communication technologies. Most of the proposed techniques can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal to noise ratio (SNR). In this paper, we investigate twofold. First, we propose an efficient system that uses a combination set of spectral characteristics and higher order moments up to eighth and higher order cumulants up to eighth as the effective features. As the classifier we used a multi-layer perceptron (MLP) neural network. In this stage we investigate different learning algorithms of MLP neural networks that some of them, such as quick prop (QP) learning algorithm, extended delta-bar-delta (EDBD), super self adaptive back propagation (SuperSAB) and conjugate gradient (CG) are proposed for the first time in the area of communication signals recognition. Experimental results show that proposed system discriminates a lot of digital communication signals with high accuracy even at very low SNRs. But a lot of features are used for this recognition. Then at the second fold, in order to reduce the complexity of the recognizer, we have proposed a novel hybrid intelligent technique. In this technique we have optimized the classifier design by bees algorithm (BA) for selection of the best features that are fed to the classifier. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Communication signal recognition bees algorithm Multi-layer perceptron Learning algorithms Spectral characteristics features Higher order moments Higher order cumulants Feature selection Optimization

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Total Energy Demand Estimation in Iran Using bees algorithm

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2011年第3期6卷 294-303页

作者： Behrang, M. A. Assareh, E. Assari, M. R. Ghanbarzadeh, A. Islamic Azad Univ Dept Mech Engn Dezful Branch Tehran Iran

Many studies are performed by researchers to estimate energy demand but the bees algorithm (BA) technique has never been used for such a study. This paper presents application of the BA technique to estimate total energy demand in Iran, based on socioeconomic indicators. The BA demand estimation models (BA-DEM) are first developed in two forms (exponential and linear) in order to estimate energy demand based on population, gross domestic product, import and export data. Then, two scenarios are designed for forecasting each socioeconomic indicator in the years 2006-2030. Finally, these two scenarios are used to forecast energy demand up to year 2030 using demand estimation models. Energy consumption in Iran from 1981-2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981-1999) and partly for testing the models (2000-2005). In order to show the accuracy of the BA, obtained results of BA-DEM models are compared with particle swarm optimization and genetic algorithm demand estimation models which are developed for the same problem.

关键词： bees algorithm demand energy forecasting Iran

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Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2017年第Pt.1期2017卷 1-15页

作者： Gokkus, Umit Yildirim, Mehmet Sinan Aydin, Metin Mutlu Manisa Celal Bayar Univ Dept Civil Engn TR-45140 Muradiye Manisa Turkey Akdeniz Univ Dept Civil Engn TR-07158 Antalya Turkey

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

关键词： TRAFFIC estimation HARBORS FORECASTING NEURAL networks (Computer science) bees algorithm

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SYNTHESIS OF THINNED LINEAR ANTENNA ARRAYS USING bees algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2011年第4期53卷 795-799页

作者： Guney, K. Onay, M. Nuh Naci Yazgan Univ Fac Engn Kayseri Turkey Erciyes Univ Civil Aviat Sch Dept Aircraft Elect & Elect TR-38039 Kayseri Turkey

In this article, we propose a method based on the bees algorithm (BA) to synthesize thinned linear antenna arrays with low sidelobe level. The BA is an optimization algorithm inspired by the behavior of the honey bees to find the optimal way of harvesting food resources around the hive. Several examples with different thinning percentages are given to show the effectiveness of the proposed method. The results of BA are compared with the results of real coded genetic algorithm, ant colony optimization, and iterative Fourier technique. (C) 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:795-799, 2011;View this article online at ***. DOI 10.1002/mop.25850

关键词： bees algorithm thinned array array synthesis sidelobe control

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A cost-sensitive classification algorithm: BEE-Miner

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KNOWLEDGE-BASED SYSTEMS 2016年 95卷 99-113页

作者： Tapkan, Pinar Ozbakir, Lale Kulluk, Sinem Baykasoglu, Adil Erciyes Univ Dept Ind Engn TR-38039 Kayseri Turkey Dokuz Eylul Univ Dept Ind Engn TR-35210 Izmir Turkey

Classification is a data mining technique which is utilized to predict the future by using available data and aims to discover hidden relationships between variables and classes. Since the cost component is crucial in most real life classification problems and most traditional classification methods work for the purpose of correct classification, developing cost-sensitive classifiers which minimize the total misclassification cost remains a subject of much interest. The purpose of this study is to present an effective solution method that configurates and evaluates learning systems from previous experiences, thus aiming to obtain decisions and predictions. Since most real life problems are cost-sensitive and developing effective direct methods for cost-sensitive multi-class classification is still an attractive area, a cost-sensitive classification method, the BEE-Miner algorithm, is proposed by utilizing the recently developed bees algorithm (BA). The main advantages of BEE-Miner are its capability to handle both binary and multi-class problems and to incorporate misclassification cost into the algorithm via generating neighbor solutions and evaluating the quality of the solutions. An extensive computational study is also performed on cost-insensitive and cost-sensitive versions of the proposed BEE-Miner algorithm and effective results on different types of problems are obtained with high test accuracy and low misclassification cost. (C) 2015 Elsevier B.V. All rights reserved.

关键词： bees algorithm Cost-sensitive classification Data mining

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A novel unsupervised bee colony optimization (UBCO) method for remote-sensing image classification: a case study in a heterogeneous marsh area

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2016年第24期37卷 5726-5748页

作者： Li, Huapeng Zhang, Shuqing Ding, Xiaohui Zhang, Ce Cropp, Roger Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Lancaster Lancaster Environm Ctr Lancaster England Griffith Univ Griffith Sch Environm Gold Coast Australia

Unsupervised image classification is an important means to obtain land-use/cover information in the field of remote sensing, since it does not require initial knowledge (training samples) for classification. Traditional methods such as k-means and Iterative self-organizing data analysis technique (ISODATA) have limitations in solving this NP-hard unsupervised classification problem, mainly due to their strict assumptions about the data distribution. The bee colony optimization (BCO) is a new type of swarm intelligence, based upon which a simple and novel unsupervised bee colony optimization (UBCO) method is proposed for remote-sensing image classification. UBCO possesses powerful exploitation and exploration capacities that are carried out by employed bees, onlookers, and scouts. This allows the promising regions to be globally searched quickly and thoroughly, without becoming trapped on local optima. In addition, it has no restrictions on data distribution, and thus is especially suitable for handling complex remote-sensing data. We tested the method on the Zhalong National Nature Reserve (ZNNR) - a typical inland wetland ecosystem in China, whose landscape is heterogeneous. The preliminary results showed that UBCO (overall accuracy = 80.81%) achieved statistically significant better classification result (McNemar test) in comparison with traditional k-means (63.11%) and other intelligent clustering methods built on genetic algorithm (unsupervised genetic algorithm (UGA), 71.49%), differential evolution (unsupervised differential evolution (UDE), 77.57%), and particle swarm optimization (unsupervised particle swarm optimization (UPSO), 69.86%). The robustness and superiority of UBCO were also demonstrated from the two other study sites next to the ZNNR with distinct landscapes (urban and natural landscapes). Enabling one to consistently find the optimal or nearly optimal global solution in image clustering, the UBCO is thus suggested as a robust method for unsuperv

关键词： REMOTE-sensing images bees algorithm MATHEMATICAL optimization DATA distribution SWARM intelligence

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Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony algorithm and Local Information Aggregation

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-18页

作者： Li, Linguo Sun, Lijuan Guo, Jian Han, Chong Li, Shujing Nanjing Univ Posts & Telecommun Sch Comp Nanjing 210003 Jiangsu Peoples R China Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur's entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image's spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposedmethod with Kapur's entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

关键词： bees algorithm MEMBERSHIP functions (Fuzzy logic) THRESHOLDING algorithms SEGMENTATION (Image processing) INFORMATION storage & retrieval systems

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