Search Results - probable optimization ((bees algorithm) OR (based algorithm))

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  1. 1
    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm by Abro, Abdul Ghani

    Published 2013
    “…Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. …”
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  2. 2
    Artificial bee colony algorithm with proposed discrete nearest neighborhood algorithm for discrete optimization problems

    Artificial bee colony algorithm with proposed discrete nearest neighborhood algorithm for discrete optimization problems by Rahimi, Amir Masoud, Ramezani-Khansari, Ehsan

    Published 2021
    “…In addition, 2 types of scout bee were used for to intensify the probability property of the algorithm. …”
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  3. 3
    Hybridization Of Deterministic And Metaheuristic Approaches In Global Optimization

    Hybridization Of Deterministic And Metaheuristic Approaches In Global Optimization by Goh, Khang Wen

    Published 2019
    “…In the analysis of the literature, Artificial Bees Colony (ABC) Algorithm has been selected as the metaheuristic approach to be improved its capability and efficiency to solve the global optimization problems. …”
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  4. 4
    Enhanced ABD-LSSVM for energy fuel price prediction

    Enhanced ABD-LSSVM for energy fuel price prediction by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2013
    “…This paper presents an enhanced Artificial Bee Colony (eABC)based on Lévy Probability Distribution (LPD) and conventional mutation. …”
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  5. 5
    Enhanced ABC-LSSVM For Energy Fuel Price Prediction

    Enhanced ABC-LSSVM For Energy Fuel Price Prediction by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2014
    “…This paper presents an enhanced Artifi cial Bee Colony (eABC) based on Lévy Probability Distribution (LPD) and conventional mutation. …”
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  6. 6
    Determining penetration limit of central distributed generation topology in radial distribution networks

    Determining penetration limit of central distributed generation topology in radial distribution networks by Suliman, Mohamed Saad Abdelgadir

    Published 2021
    “…The biogeography based optimization method has been proven to have better performance than artificial bee colony, genetic algorithm, particle swarm optimization, hybrid of particle swarm optimization and constriction factor approach, and hybrid of ant colony optimization and artificial bee colony methods in terms of active power loss reduction. …”
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  7. 7
    A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems

    A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems by Tam, Jun Hui, Ong, Zhi Chao, Ismail, Zubaidah, Ang, Bee Chin, Khoo, Shin Yee

    Published 2019
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  8. 8
    Improved flower pollination optimization algorithm based on swap operator and dynamic switch probability selection

    Improved flower pollination optimization algorithm based on swap operator and dynamic switch probability selection by Iqbal, Muhammad

    Published 2023
    “…This research proposed an algorithm which is based on dynamic switch probability to control the balance between exploration and exploitation which increases its searchability. …”
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  9. 9
    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern

    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

    Published 2012
    “…GA is a widely used optimization algorithm based on the concept of survival of the fittest. …”
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  10. 10
    Q-learning whale optimization algorithm for test suite generation with constraints support

    Q-learning whale optimization algorithm for test suite generation with constraints support by Hassan, Ali Abdullah, Salwani, Abdullah, Kamal Z., Zamli, Rozilawati, Razali

    Published 2023
    “…This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). …”
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  11. 11
    Sizing of a standalone photovoltaic water pumping system using a multi-objective evolutionary algorithm

    Sizing of a standalone photovoltaic water pumping system using a multi-objective evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A., Natsheh E.M.

    Published 2023
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  12. 12
    Multiobjective differential evolution algorithm-based sizing of a standalone photovoltaic water pumping system

    Multiobjective differential evolution algorithm-based sizing of a standalone photovoltaic water pumping system by Muhsen D.H., Ghazali A.B., Khatib T.

    Published 2023
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  13. 13
    Tree-based contrast subspace mining method

    Tree-based contrast subspace mining method by Florence Sia Fui Sze

    Published 2020
    “…Hence, this thesis presents the optimization of parameters values for the tree-based method by genetic algorithm. …”
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  14. 14
    Optimizing S-box generation based on the Adaptive Agent Heroes and Cowards Algorithm

    Optimizing S-box generation based on the Adaptive Agent Heroes and Cowards Algorithm by Kamal Z., Zamli

    Published 2021
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  15. 15
    Power allocation optimization for oam-mimo systems with unaligned antennas

    Power allocation optimization for oam-mimo systems with unaligned antennas by Xiong, Xusheng, Li, Qiang, Ge, Xiaohu, Sali, Aduwati, Mohd Ali, Borhanuddin

    Published 2022
    “…Numerical results show that the error probability reduction of the OAM-MIMO transmission system by using the proposed optimization algorithm is up to 26.1 dB.…”
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  16. 16
    Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model

    Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model by Selva A.M., Yahaya M.S., Azis N., Ab Kadir M.Z.A., Jasni J., Yang Ghazali Y.Z.

    Published 2023
    Conference Paper
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  17. 17
    The effect of GA parameters on the performance of GA-based QoS routing algorithm

    The effect of GA parameters on the performance of GA-based QoS routing algorithm by Yussof S., See O.H.

    Published 2023
    “…Genetic algorithm (GA) is a powerful search and optimization algorithm inspired by the theory of genetics and natural selection. …”
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  18. 18
    The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment

    The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment by Kok, Kai Yit

    Published 2016
    “…Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). …”
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  19. 19
    An empirical study of density and distribution functions for ant swarm optimized rough reducts

    An empirical study of density and distribution functions for ant swarm optimized rough reducts by Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah

    Published 2011
    “…Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. …”
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  20. 20
    Dynamic probability selection for flower pollination algorithm based on metropolis-hastings criteria

    Dynamic probability selection for flower pollination algorithm based on metropolis-hastings criteria by Zamli, Kamal Zuhairi, Din, Fakhrud, Nasser, Abdullah, Ramli, Nazirah, Mohamed, Noraini

    Published 2021
    “…However, FPA still suffers from variability of its performance as there is no one size that fits all values for pa, depending on the characteristics of the optimization function. This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. …”
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