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artificial bee colony with Levy Flights for Parameter Estimation of 3-p Weibull Distribution

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE 2020年第3期44卷 851-864页

作者： Yonar, Aynur Pehlivan, Nimet Yapici Selcuk Univ Sci Fac Stat Dept Konya Turkey

Three-parameter (3-p) Weibull distribution is widely used to model failure distribution in reliability studies, and so the estimation of the parameters of this distribution is very crucial. The maximum likelihood (ML) method is the most popular method among parameter estimation methods. However, likelihood equations do not have explicit solutions for the 3-p Weibull distribution. Therefore, using metaheuristic methods is logical to obtain the ML estimation of 3-p Weibull distribution. The artificial bee colony (ABC) is one of the very simple, robust and population-based stochastic metaheuristic algorithms. The aim of this study is to obtain ML estimations of parameters of 3-p Weibull distribution by suggesting ABC with Levy flights (LABC) which improve the exploitation ability of the ABC algorithm. Furthermore, the ML estimation results of the LABC algorithm are compared with other well-known metaheuristic algorithms, standard ABC, particle swarm optimization, particle swarm optimization with Levy flights, simulated annealing, differential evolution and genetic algorithm through simulation studies and a real-data application. The results show that the suggested LABC algorithm gives more accurate ML estimations than the other metaheuristic algorithms for the parameter estimation of 3-p Weibull distribution.

关键词： Weibull distribution Maximum likelihood artificial bee colony algorithm Levy flights Metaheuristic algorithms Parameter estimation

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Implementation of artificial bee colony algorithm on Maximum Power Point Tracking for PV Modules

Implementation of Artificial Bee Colony Algorithm on Maximum...

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8th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Bilal, Babar Univ Politehn Bucuresti Fac Elect Engn Bucharest Romania

ISBN: (纸本)9781467359801;9781467359795

Renewable energy has become of utmost importance in the current wake of decreasing levels of fossil fuels and also because of the high percentages of CO2 build-up in the environment. Nowadays there is an increasing trend in the use of solar energy by using photovoltaic (PV). In this paper artificial bee colony algorithm is used for Maximum Power Point Tracking and its performance is compared with Perturb and Observe algorithm.

关键词： photovoltaic modules maximum power point tracking artificial bee colony algorithm

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Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques

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JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020年第2期100卷 817-824页

作者： Sabanci, Kadir Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn Karaman Turkey

BACKGROUNDIn this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG and 150 SDG being used for classification. Thanks to the constructed imaging setup, photos of the 300 wheat grains are obtained. Seventeen visual features of each wheat grain are extracted by image-processing techniques and evaluated in three different groups of dimension, texture and pattern as visual parameters. artificial bee colony (ABC) optimization-based artificial neural network (ANN) and extreme learning machine (ELM) algorithms are implemented to classify the damaged wheat grains. RESULTSA correlation-based feature selection (CFS) technique is also utilized to find the most effective among the 17 features. In the classification process using five selected features, the mean absolute error (MAE) and root mean square error (RMSE) values for ABC-based ANN are calculated as 0.00174 and 0.00433 respectively. The proposed technique is integrated into graphical user interface (GUI) software to construct an effective detection system for practical use. CONCLUSIONThe results indicate that, thanks to the modified ANN algorithm and implemented CFS algorithm, the detection accuracy of damaged wheat grains is considerably increased. (c) 2019 Society of Chemical Industry

关键词： artificial bee colony algorithm artificial neural network correlation feature selection extreme learning machine sunn pest local binary pattern wheat classification

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A Novel Approach Based on Hybrid algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

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Computer Systems Science & Engineering 2022年第10期43卷 259-273页

作者： C.Ram Kumar K.Murali Krishna Mohammad Shabbir Alam K.Vigneshwaran Sridharan Kannan C.Bharatiraja Department of Electronics and Communication Engineering Dr.N.G.P.Institute of TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Vignan’s Institute of Information TechnologyDuvvadaVisakhapatnam530049India Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJizan45142Kingdom of Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering JKK Munirajah College of TechnologyErode638506India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data *** clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster *** major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor *** proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and artificial bee colony(ABC)algorithm for the better identification of cluster *** performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

关键词： Wireless sensor network cluster cluster head hybrid model glowworm swarm optimization artificial bee colony algorithm energy consumption

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artificial bee colony algorithm for the Parallel Test Tasks Scheduling

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International Conference on Manufacturing Engineering and Automation (ICMEA 2012)

作者： Chen Li'an Xiao Mingqing Yu Hang Zhao Liangliang Air Force Univ Automat Test Syst Lab Xian 710038 Peoples R China

ISBN: (纸本)9783037855300

Parallel test is the key technology of the NxTest technology and the parallel test tasks scheduling is one of the important part of parallel test. The mathematical model of the problem was introduced, according to the advantage of solving the problem of dynamic scheduling with artificial bee colony algorithm;an approach of parallel test scheduling based on artificial bee colony algorithm is brought forward. An example was given, the result of simulation shows that this algorithm's constringency fast and the result has a high precision, it is an efficient way of solving the problem of optimized parallel test tasks scheduling.

关键词： Parallel test Test tasks scheduling artificial bee colony algorithm

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Selecting and prioritizing the electricity customers for participating in demand response programs

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第10期16卷 2086-2096页

作者： Asghari, Pardis Zakariazadeh, Alireza Siano, Pierluigi Univ Sci & Technol Mazandaran Dept Elect & Comp Engn POB 48518-78195 Behshahr Iran Univ Salerno Dept Management & Innovat Syst Via Giovanni Paolo II132 Fisciano Italy Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa

Demand response (DR) provides an opportunity for customers to play an important role in the operation of the electricity grid by reducing or shifting their electricity usage during peak periods. However, selecting customers to participate in DR programs is challenging. To solve this problem, typical load profiles should be characterized by data mining techniques such as clustering algorithms. Traditional clustering algorithms manually determine the centre of clusters and that the selected centre of clusters may fall into a local optimum. Here, to overcome these issues, a new clustering algorithm based on the Density Peak Clustering algorithm (DPC) and artificial bee colony algorithm (ABC) which is called A-DPC, is implemented to optimally determine the representative load curves. Moreover, by introducing a new priority index, the eligible residential customers are selected for participating in DR programs. Also, to meet the sufficient load reduction in a DR event, the proposed approach suggests a plenty number of residential customers to be called. The result evidence that A-DPC has a stronger global search ability to optimally select the centre of clusters if compared to other clustering algorithms.

关键词： search problems A-DPC artificial bee colony algorithm optimisation pattern clustering peak periods electricity usage representative load curves typical load profiles Density Peak Clustering algorithm electricity grid data mining artificial bee colony algorithm eligible residential customers DR event electricity customers demand response programs demand side management DR programs power engineering computing

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Hepatitis C Virus Detection Model by Using Random Forest, Logistic-Regression and ABC algorithm

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IEEE ACCESS 2022年 10卷 91045-91058页

作者： Li, Tzuu-Hseng S. Chiu, Huan-Jung Kuo, Ping-Huan Natl Cheng Kung Univ Dept Elect Engn aiRobots Lab Tainan 70101 Taiwan Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan

This study proposes an automatic classifier for detecting the multiclass probabilities of hepatitis C virus (HCV) incidence based on patients' blood attributes. The purpose of this study is to establish an artificial intelligence-based model that can identify HCV patients and detect the disease in early stage for future treatments. This model can be applied by using clinical data and keeps the performance from imbalanced datasets. The innovation in this article lies in considering the "unbalanced data" existing in medical record-based clinical data. Synthetic minority oversampling technique (SMOTE) algorithm was further employed to derive corresponding solutions. This objective was achieved using a cascade two-stage method combining the random forest (RF) and logistic regression (LR) algorithms. Two models were trained by applying the RF (Model 1) and LR (Model 2) to raw and preprocessed data, respectively. The artificial bee colony (ABC) algorithm was then used to determine the optimal threshold value required for filtering and separation, that is, the optimal combination of both models. The two-stage mixing algorithm combines algorithms of different search dimensions, thus integrating the strengths of those algorithms. The critical threshold value for separating Model 1 and Model 2 was obtained through an optimized search using the ABC algorithm. After conducting 10-fold Monte Carlo cross-validation experiments 50 times (for mean values), data from the recent pandemic were used to verify the proposed method. To evaluate the quantitative results, indicators, such as prediction accuracy, precision, recall, F1-score, and Matthews correlation coefficient, were compared with those of the latest algorithms used in relevant fields. The results indicate that the proposed model, named Cascade RF-LR (with SMOTE), can be used to detect the multiclass probabilities of HCV incidence using the ABC algorithm, thereby improving the effectiveness of relevant treatments.

关键词： Liver diseases Classification tree analysis Random forests Data models Classification algorithms artificial bee colony algorithm Medical diagnostic imaging Monte Carlo methods Sampling methods Random forest logistic regression two-stage mixing ABC algorithm 10-fold Monte-Carlo cross-validation synthetic minority oversampling technique

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Joint resource allocation for QoE optimization in large-scale NOMA-enabled multi-cell networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第1期15卷 689-702页

作者： Jia, Jie Xu, Yao Du, Zhenjun Chen, Jian Wang, Qinghu Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Minist Educ Engn Res Ctr Secur Technol Complex Network Syst Shenyang 110819 Peoples R China SIASUN Robot Automat CO Ltd Shenyang Peoples R China

The non-orthogonal multiple access (NOMA) technology has been considered as a promising technology for the upcoming six-generation mobile communication networks (6G). However, with the increasing complexity of this access technology, how to allocate the limited network resources effectively for massive number of connectivity becomes more and more challenging. The QoE optimization problem including both the sub-channel (SC) assignment and power allocation is formulated. In order to cope with it, we first decouple it into two sub-problems: the UE-BS association, SC assignment sub-problem and the power allocation sub-problem. We thus propose genetic algorithm (GA) for UE-BS association, SC assignment and artificial bee colony (ABC) algorithm for power allocation. The corresponding constraints satisfying mechanisms are also proposed to ensure the feasibility of individual bee colony in each iteration, thus to accelerate the convergence. The simulation results show that the proposed power allocation scheme can converge to the optimal solution quickly, and the MOS can be increased by increasing the number of users (UE), and SCs.

关键词： artificial bee colony algorithm Genetic algorithm Mean opinion score Non-orthogonal multiple access Power allocation Quality of experience NONORTHOGONAL MULTIPLE-ACCESS POWER algorithm DOWNLINK SYSTEMS

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An extreme bias-penalized forecast combination approach to commodity price forecasting

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INFORMATION SCIENCES 2022年 615卷 774-793页

作者： Zhang, Yifei Wang, Jue Yu, Lean Wang, Shouyang Chinese Acad Sci Acad Math & Syst Sci CFS MADIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China

Forecast combination, a well-established technique for improving forecasting accuracy, investigates the integration of competing forecasts to produce a composite superior to individual forecasts. In this study, we propose a novel forecast combination method that would reduce overfitting risk and improve forecast's generalization ability. To capture the extreme bias of a forecast in combination process, we define a measurement PaR for forecast combination. A novel PaR-based loss function with an elastic net is proposed that can effectively trade off the sparsity of weights to mitigate the risk of underfitting or over -fitting. An improved artificial bee colony algorithm-based optimization method is intro-duced to achieve the optimal weights. The experimental results on gold, silver and crude oil price data demonstrate that the proposed forecast combination approach can outper-form not only individual models but also combination approaches like simple averaging and other competitive benchmarks. The MAPE achieved by the presented method could decrease by 10.98%, 5.03% and 10.28% in gold, silver and crude oil price forecasting respec-tively, compared to the best individual model.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Forecast combination Elastic net Extreme bias Weight-sparsity artificial bee colony algorithm

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MODELING OF EFFECTIVE PARAMETERS FOR CAPACITY PREDICTION AT SIGNALIZED INTERSECTION LANES

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BALTIC JOURNAL OF ROAD AND BRIDGE ENGINEERING 2022年第4期17卷 35-62页

作者： Aydin, Metin Mutlu Ondokuz Mayis Univ Fac Engn Dept Civil Engn Samsun Turkey

Current capacity manuals do not allow comprehensively evaluating negative effects on lane capacity caused by undisciplined vehicle movements and lane utilization, such as failure to obey distance rules, lane blockage caused by roadside parking effect, formation of an extra lane using in the emergency lane, etc., which are mostly observed in undeveloped and developing countries. Irregularities of the traffic flow caused by undisciplined movements and lane utilization result in decreased capacity or traffic change on the urban lanes. To overcome this problem, a lane-based study was carried out to determine the relation among effective parameters and their effect on lane capacity. In order to model the impact of these parameters, a comprehensive study was conducted in two cities in Turkey. Two different methods (statistical analysis and metaheuristic search algorithm) were used for this purpose and new more reasonable lane capacity estimation models (ALLCEM-1 and ALLCEM-2) were developed by examining all local conditions. The results proved that both examined methods are effective in modelling lane capacity of signalized intersections. It was also found that such parameters as the type of intersection (either a roundabout or not), effective green time, saturation flow rate, traffic volume, heavy vehicle ratio, and the number of actively used lanes have a major impact on the accuracy of prediction of road capacity.

关键词： artificial bee colony algorithm lane-based capacity estimation ordinary least squares regression traffic volume signalized intersection

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