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  1. 1

    A study of packet scheduling algorithms in long term evolution-advanced by Ul Islam Mattoo, Mohd Mueen, Mohd. Ramli, Huda Adibah

    Published 2019
    “…Majority of the previous studies of packet scheduling algorithms for LTE-Advanced did not take the effect of channel impairments into account. …”
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    Article
  2. 2

    A study of packet scheduling with mobile cellular channel impairments by Mohd. Ramli, Huda Adibah, Sandrasegaran, Kumbesan, Abdullah, Khaizuran

    Published 2012
    “…This paper studies a number of low complexity real time packet scheduling algorithms developed for the legacy mobile cellular systems. …”
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    Proceeding Paper
  3. 3

    A study of packet scheduling with mobile cellular channel impairments by Mohd. Ramli, Huda Adibah, Sandrasegaran, Kumbesan, Abdullah, Khaizuran

    Published 2012
    “…This paper studies a number of low complexity real time packet scheduling algorithms developed for the legacy mobile cellular systems. …”
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    Proceeding Paper
  4. 4

    Bats echolocation-inspired algorithms for global optimisation problems by Nafrizuan, Mat Yahya

    Published 2016
    “…The aim of the research is to introduce novel form of swarm intelligence algorithms based on real echolocation behaviour of bats. …”
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    Thesis
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    Channel-aware downlink scheduling for quality of service in long term evolution network by Najim, Alaa Omer

    Published 2018
    “…The output results indicate that the proposed DoRA scheduling algorithm outperforms DBWPF in providing a minimum level of end-to-end delay.…”
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    Thesis
  7. 7

    Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat by Naji Maqhat, Bakeel Hussein

    Published 2016
    “…The aim of this thesis is to develop a fair and efficient packet scheduling and adaptive bandwidth allocation algorithms to support QoS for a diverse service class for A-MSDU aggregation in IEEE 802.1 In network. …”
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    Thesis
  8. 8

    HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION by Kappagantula, S., Vojjala, S., Iyer, A.A., Velidi, G., Emani, S., Vandrangi, S.K.

    Published 2023
    “…The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. …”
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  9. 9

    Hybrid bat algorithm for minimum dominating set problem by Abed, S.A., Rais, H.M.

    Published 2017
    “…This method uses population-based approach called bat algorithm (BA) which explore a wide area of the search space, thus it is capable in the diversification procedure. …”
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    Article
  10. 10

    Solving the minimum dominating set problem of partitioned graphs using a hybrid bat algorithm by Abed, S.A., Rais, H.M.

    Published 2020
    “…This paper investigates the swarm intelligence behaviour represented by a population-based approach called the bat algorithm (BA) to find the smallest set of nodes that dominate the graph. …”
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    Interference and Priority Aware Coexistence (IPC) Algorithm for Link Scheduling in IEEE 802.15.6 Based WBANs by Khan, F.N., Ahmad, R., Ahmed, W., Alam, M.M., Drieberg, M.

    Published 2019
    “…This paper presents an Interference and Priority aware Coexistence (IPC) algorithm to ensure coexistence between multiple WBANs communicating within each other transmission range. …”
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    Article
  13. 13

    Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems by Salman, Mustafa Ismael

    Published 2015
    “…Consequently, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB. …”
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    Thesis
  14. 14

    Review of Multi-Objective Swarm Intelligence Optimization Algorithms by Yasear, Shaymah Akram, Ku Mahamud, Ku Ruhana

    Published 2021
    “…The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. …”
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  15. 15

    Extended bat algorithm for PID controller tuning of wheeled mobile robot and swarm robotics target searching strategy by Nur Aisyah Syafinaz, Suarin

    Published 2020
    “…Extended Bat Algorithm (EBA) has been chosen as swarm intelligent based method for this research study. …”
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    Thesis
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    Quantum Inspired Computational Intelligence Techniques for Combined Economic Emission Dispatch Problem by MAHDI, FAHAD PARVEZ

    Published 2017
    “…Quantum computing phenomenon is integrated with swarm intelligence-based PSO and bat algorithm (BA) to make these algorithms computationally more powerful and robust.…”
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    Thesis
  18. 18

    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

    Published 2024
    “…The firefly algorithm remains a feasible alternative for shallow architectural network models, while metaheuristic algorithms such as the Particle swarm algorithm and Bat algorithm are better options for deeper architectural network models. …”
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    Thesis
  19. 19

    A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems by Yaseen, Zaher, Ehteram, Mohammad, Hossain, Md., Fai, Chow, Koting, Suhana, Mohd, Nuruol Syuhadaa, Jaafar, Wan Zurina Wan, Afan, Haitham Abdulmohsin, Lai, Sai Hin, Zaini, Nuratiah, Ahmed, Ali, El-Shafie, Ahmed

    Published 2019
    “…The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. …”
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  20. 20

    Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management by Julius, Beneoluchi Odili, M. N. M., Kahar, Noraziah, Ahmad, M., Zarina, Riaz, Ul Haq

    Published 2017
    “…After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. …”
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