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  1. 1
    Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman

    Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman by Isman, Muhammad Iskandar

    Published 2017
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  2. 2
    Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency

    Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency by Sarkar, Md. Rasel, Julai, Sabariah, Chong, Wen Tong, Toha @ Tohara, Siti Fauziah

    Published 2019
    “…The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms…”
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  3. 3
    Traditional marble game using ant colony optimization / Muhammad Izzat Imran Che Isa

    Traditional marble game using ant colony optimization / Muhammad Izzat Imran Che Isa by Che Isa, Muhammad Izzat Imran

    Published 2017
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  4. 4
    Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

    Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency by Sarkar, Md Rasel, Julai, Sabariah, Chong, Wen Tong, Toha, Siti Fauziah

    Published 2019
    “…The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R 2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms. © 2019 by the authors.…”
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  5. 5
    Secure ACO-Based Wireless Sensor Network Routing Algorithm for IoT

    Secure ACO-Based Wireless Sensor Network Routing Algorithm for IoT by Sharmin, Afsah, Anwar, Farhat, Motakabber, S. M. A., Hassan Abdalla Hashim, Aisha

    Published 2021
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  6. 6
    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar

    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

    Published 2019
    “…There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient (�436�45D ). …”
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  7. 7
    Optimization in active vibration control: virtual experimentation using COMSOL multiphysics - MATLAB integration

    Optimization in active vibration control: virtual experimentation using COMSOL multiphysics - MATLAB integration by Md Nor, Khairul Affendy, Abdul Muthalif, Asan Gani, Wahid, Azni N.

    Published 2014
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  8. 8
    Interacted Multiple Ant Colonies for Search Stagnation Problem

    Interacted Multiple Ant Colonies for Search Stagnation Problem by Aljanabi, Alaa Ismael

    Published 2010
    “…Ant Colony Optimization (ACO) is a successful application of swarm intelligence. …”
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  9. 9
    A new multiple ant colonies optimization algorithm utilizing average pheromone evaluation mechanism

    A new multiple ant colonies optimization algorithm utilizing average pheromone evaluation mechanism by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

    Published 2008
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  10. 10
    Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid

    Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid by Ku-Mahamud, Ku Ruhana, Ramli, Razamin, Yusof, Yuhanis, Mohamed Din, Aniza, Mahmuddin, Massudi

    Published 2012
    “…Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. …”
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  11. 11
    Comparison between ant colony and genetic algorithm using traveling salesman problem

    Comparison between ant colony and genetic algorithm using traveling salesman problem by Abduljabbar, Zaid Ammen, Khalefa, Mustafa S., A. Jabar, Marzanah

    Published 2013
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  12. 12
    Interacted multiple ant colonies optimization approach to enhance the performance of ant colony optimization algorithms

    Interacted multiple ant colonies optimization approach to enhance the performance of ant colony optimization algorithms by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita

    Published 2010
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  13. 13
    Optimizing large scale combinatorial problems using multiple ant colonies algorithm based on pheromone evaluation technique

    Optimizing large scale combinatorial problems using multiple ant colonies algorithm based on pheromone evaluation technique by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md Norwawi, Norita

    Published 2008
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  14. 14
    Ant colony algorithm for job scheduling in grid computing

    Ant colony algorithm for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana, Abdul Nasir, Husna Jamal

    Published 2010
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  15. 15
    Brain Machine Interface Controlled Robot Chair

    Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

    Published 2010
    “…A particle swarm optimization based algorithm is proposed to train the neural networks. …”
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  16. 16
    Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing

    Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

    Published 2015
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  17. 17
    Performance analysis of ant colony's algorithm: load-balancing in QoS-based wireless mesh networks routing

    Performance analysis of ant colony's algorithm: load-balancing in QoS-based wireless mesh networks routing by Moghanjoughi, Ayyoub Akbari, Khatun, Sabira, Mohd Ali, Borhanuddin, Raja Abdullah, Raja Syamsul Azmir

    Published 2008
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  18. 18
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
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  19. 19
    Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC)

    Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC) by Ahmad, Faudziah, Ku-Mahamud, Ku Ruhana, Sainin, Mohd Shamrie, Airuddin, Ahmad

    Published 2015
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  20. 20
    Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification

    Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification by Nuaimi, Zakaria Noor Aldeen Mahmood Al, Abdullah, Rosni

    Published 2017
    “…Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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