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  1. 1

    Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification by Hijazi, Musab, Zeki, Akram M., Ismail, Amelia Ritahani

    Published 2023
    “…In this paper, the filter method chi square and the Artificial Bee Colony) ABC algorithm were both used as FS methods . …”
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    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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  3. 3

    Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network by Muhammad Sidik, Siti Syatirah

    Published 2023
    “…Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. …”
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    Thesis
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    Honey bee based trust management system for cloud computing by Firdhous, Mohamed, Ghazali, Osman, Hassan, Suhaidi, Harun, Nor Ziadah, Abas, Azizi

    Published 2011
    “…In this paper, the authors propose the concept that honey bee algorithm which has been developed to solve complex optimization problems can be successfully used to address this issue.The authors have taken a closer look at the optimization problems that had been solved using the honey bee algorithm and the similarity between these problems and the cloud computing environment.Thus concluding that the honey bee algorithm could be successfully used to solve the trust management issue in cloud computing.…”
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    Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

    Published 2014
    “…This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. …”
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    Thesis
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    Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data by Hassan, S., Jaafar, J., Khanesar, M.A., Khosravi, A.

    Published 2016
    “…This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed hybrid learning algorithm utilizes the combination of extreme learning machine (ELM) and artificial bee colony optimization (ABC) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. …”
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    An improved dynamic load balancing for virtualmachines in cloud computing using hybrid bat and bee colony algorithms by Ullah, Arif

    Published 2021
    “…Therefore, to overcome these problems, this study proposed an improved dynamic load balancing technique known as HBAC algorithm which dynamically allocates task by hybridizing Artificial Bee Colony (ABC) algorithm with Bat algorithm. …”
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    Optimum tie switches allocation and DG placement based on maximisation of system loadability using discrete artificial bee colony algorithm by Aman, M.M., Jasmon, G.B., Mokhlis, Hazlie, Bakar, Ab Halim Abu

    Published 2016
    “…This study presents a new approach for simultaneous optimum distributed generation (DG) placement and optimum tie-switch allocation based on maximisation of system loadability using discrete artificial bee colony algorithm. The proposed algorithm is tested on 16-Bus, 33-Bus and 69-Bus radial distribution test system. …”
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    Article
  12. 12

    An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons by Ghanem, Waheed Ali H. M., Aman, Jantan, Ahmed Ghaleb, Sanaa Abduljabbar, Naseer, Abdullah B.

    Published 2020
    “…This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. …”
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    Article
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    Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi M... by Mohammed, Daw Saleh Sasi

    Published 2016
    “…In order produce a global optimal solution of AUC, the self-adaptive strategy was proposed to serve as a new mutation technique responsible to provide a new population for discrete artificial bee colony. The newly designed algorithm is termed as the discrete artificial bee colony associated with selfadaptive strategy (DABCSAS). …”
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    Thesis
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    Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

    Published 2025
    “…This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. …”
    Article
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    Intelligent Tuning of PID Controller for Double-Link Flexible Robotic Arm Manipulator by Artificial Bee Colony Algorithm by Jamali, Annisa, Mat Darus, I.Z., Yatim, H.M, A. Talib, M. H., Hadi, M.S, Tokhi, M.O.

    Published 2020
    “…This research focuses on the development of intelligent controller utilizing artificial bee colony (ABC) algorithm to tune proportional integral derivative (PID) parameters for controlling two-link flexible manipulator (TLFRM). …”
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    Book Chapter
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    Computational intelligence of probabilistic simulation in demand side management for avoided utility cost improvisation in a generation operating system planning / Daw Saleh Sasi M... by Sasi Mohamme, Daw Saleh

    Published 2017
    “…In order produce a global optimal solution of AUC, the self-adaptive strategy was proposed to serve as a new mutation technique responsible to provide a new population for discrete artificial bee colony. The newly designed algorithm is termed as the discrete artificial bee colony associated with selfadaptive strategy (DABCSAS). …”
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    Book Section
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    Application of LSSVM by ABC in energy commodity price forecasting by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2014
    “…The importance of the hyper parameters selection for a kernel-based algorithm, viz.Least Squares Support Vector Machines (LSSVM) has been a critical concern in literature.In order to meet the requirement, this work utilizes a variant of Artificial Bee Colony (known as mABC) for hyper parameters selection of LSSVM.The mABC contributes in the exploitation process of the artificial bees and is based on Levy mutation.Realized in crude oil price forecasting, the performance of mABC-LSSVM is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSPE) and compared against the standard ABC-LSSVM and LSSVM optimized by Genetic Algorithm. …”
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    Conference or Workshop Item
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    LSSVM parameters tuning with enhanced artificial bee colony by Mustaffa, Zuriani, Yusof, Yuhanis

    Published 2014
    “…To date, exploring an efficient method for optimizing Least Squares Support Vector Machines (LSSVM) hyperparameters has been an enthusiastic research area among academic researchers.LSSVM is a practical machine learning approach that has been broadly utilized in numerous fields. To guarantee its convincing performance, it is crucial to select an appropriate technique in order to obtain the optimized hyper-parameters of LSSVM algorithm.In this paper, an Enhanced Artificial Bee Colony (eABC) is used to obtain the ideal value of LSSVM’s hyper parameters, which are regularization parameter, γ and kernel parameter, σ2.Later, LSSVM is used as the prediction model. …”
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    Article
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