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  1. 1
    Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman

    Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman by Azman, Muhammad Izzat Azri

    Published 2017
    “…This project used Artificial Bee Colony Algorithms (ABC) by adapting the food foraging behaviour of bee in honey bee and find a suitable house for home buyer based on their requirement. …”
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  2. 2
    The design and applications of the african buffalo algorithm for general optimization problems

    The design and applications of the african buffalo algorithm for general optimization problems by Odili, Julius Beneoluchi

    Published 2017
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  3. 3
    Artificial Bee Colony Algorithm for Pairwise Test Generation

    Artificial Bee Colony Algorithm for Pairwise Test Generation by Alazzawi, Ammar K., Homaid, Ameen A. Ba, Alomoush, Alaa A., Alsewari, Abdulrahman A.

    Published 2017
    “…PABC progresses as a means to achieve the effective use of the artificial bee colony algorithm for pairwise testing reduction.…”
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  4. 4
    Stray animal complaint system using geolocation technique with rule-based Algorithm / Albin Lemuel Kushan … [et al.]

    Stray animal complaint system using geolocation technique with rule-based Algorithm / Albin Lemuel Kushan … [et al.] by Kushan, Albin Lemuel, Mahadi, Mohamad Tajuddin, Zulhemay, Mohd Nabil, Sadikan, Siti Fairuz Nurr, Mishan, Mohd Taufik

    Published 2021
    “…In addition to that, geolocation technique is implemented to provide a better information in terms of the complainant’s location, and together with the utilization of a rule-based algorithms based on priority to provide the local council a better insight on the issue. …”
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  5. 5
    AirTransit Trip Planner Application

    AirTransit Trip Planner Application by Azhar, Muhamad Hafiz Mughni

    Published 2013
    “…The algorithm also need to consider the distance of the tourism places from user’s current location and also time taken to go there. …”
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  6. 6
    Application of the bees algorithm to the selection features for manufacturing data

    Application of the bees algorithm to the selection features for manufacturing data by Pham, D.T, Mahmuddin, Massudi, Otri, S., Al-Jabbouli, H.

    Published 2007
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  7. 7
    Semantic-Based Scalable Decentralized Resource Discovery

    Semantic-Based Scalable Decentralized Resource Discovery by Mahamat Issa , Hassan, Azween, Abdullah

    Published 2010
    “…DA describes resource capabilities, and RA carries resource requests that are needed for some applications. We develop a RD algorithm that optimizes the search of the resources on the network. …”
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  8. 8
    Machine condition monitoring and fault diagnosis using spectral analysis techniques

    Machine condition monitoring and fault diagnosis using spectral analysis techniques by Salami, Momoh Jimoh Eyiomika, Abdul Muthalif, Asan Gani, Pervez, T.

    Published 2001
    “…There is need to continuously monitor the conditions of complex, expensive and process-critical machinery in order to detect its incipient breakdown as well as to ensure its high performance and operating safety. …”
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  9. 9
    An efficient Dynamic Addressing based routing protocol for Underwater Wireless Sensor Networks

    An efficient Dynamic Addressing based routing protocol for Underwater Wireless Sensor Networks by Ayaz, Muhammad, Abdullah, Azween, Faye , Ibrahima, Batira, Yasir

    Published 2012
    “…In order to handle the problem of node mobility, we proposed a Hop-by-Hop Dynamic Addressing Based (H2-DAB) routing protocol, where every node in the network will be assigned a routable address in a quick and efficient way without requiring an explicit configuration or any dimensional location information. …”
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  10. 10
    Semantic-Based Scalable Decentralized Grid Resource Discovery

    Semantic-Based Scalable Decentralized Grid Resource Discovery by Mahamat Issa , Hassan, Azween, Abdullah

    Published 2009
    “…DA describes resource capabilities, and RA carries resource requests that are needed for some applications. We develop a RD algorithm that optimizes the search of the resources on the network. …”
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  11. 11
    ABC Algorithm for Combinatorial Testing Problem

    ABC Algorithm for Combinatorial Testing Problem by Alsewari, Abdulrahman A., Alazzawi, Ammar K., Rassem, Taha H., M. Nomani, Kabir, Homaid, Ameen A. Ba, Alsariera, Yazan A., Tairan, Nasser M., Kamal Z., Zamli

    Published 2017
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  12. 12
    Application of the Bees Algorithm to find optimal drill path sequence

    Application of the Bees Algorithm to find optimal drill path sequence by Zainal Abidin, Muhammad Harith, Kamaruddin, Shafie, Adam Malek, Afiqah, Sukindar, Nor Aiman

    Published 2024
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  13. 13
    Comparison Between the Bees Algorithm and Genetic Algorithm Model in Manpower Allocation on Cell Loading Problem

    Comparison Between the Bees Algorithm and Genetic Algorithm Model in Manpower Allocation on Cell Loading Problem by AlMahasneh, Hossam Sayel

    Published 2010
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  14. 14
    Test Data Generation for Event Driven System Using Bees Algorithm

    Test Data Generation for Event Driven System Using Bees Algorithm by Mohammed Zabil, Mohd H., Kamal Z., Zamli

    Published 2013
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  15. 15
    Optimization of drilling path using the bees algorithm

    Optimization of drilling path using the bees algorithm by Kamaruddin, Shafie, Rosdi, Mohamad Naqiuddin, Sukindar, Nor Aiman

    Published 2021
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  16. 16
    Metaheuristic algorithms for solving lot-sizing and scheduling problems in single and multi-plant environments / Maryam Mohammadi

    Metaheuristic algorithms for solving lot-sizing and scheduling problems in single and multi-plant environments / Maryam Mohammadi by Mohammadi, Maryam

    Published 2015
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  17. 17
    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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  18. 18
    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 Al Nuaimi, Zakaria Noor Aldeen Mahmood, Abdullah, Rosni

    Published 2017
    “…By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima.Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues.Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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  19. 19
    An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

    An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation by Alrosan, Ayat, Alomoush, Waleed, Norwawi, Norita, Alswaitti, Mohammed, Makhadmeh, Sharif Naser

    Published 2024
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  20. 20
    Bee foraging behaviour techniques for grid scheduling problem

    Bee foraging behaviour techniques for grid scheduling problem by Alyaseri, Sana, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.These resources are collected together to make a huge computing power.Job scheduling problem is one of the key issues in grid computing and failing to look into grid scheduling results in uncompleted view of the grid computing.Achieving optimized performance of grid system, and matching application requirements with available computing resources, are the objectives of grid job scheduling.Bee colony approaches are more adaptive to grid scheduling due to high heterogeneous and dynamic nature of resources and applications in grid.These algorithms have shown encouraging results in terms of time and cost.This paper presents some resent research activities inspired by bee foraging behavior for grid job scheduling especially ABC and BCO approaches.Different original studies related to this area are briefly described along with their comparisons against them and results.The review summary of their derived algorithms and research efforts is done.…”
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