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  1. 1
    Knowledge base tuning using genetic algorithm for fuzzy behavior-based autonomous mobile robot

    Knowledge base tuning using genetic algorithm for fuzzy behavior-based autonomous mobile robot by Adriansyah, Andi, H. M. Amin, Shamsudin

    Published 2005
    “…This paper presents a new schema to overcome behavior-based problems based on Fuzzy Logic Controller (FLC) where the fuzzy knowledge bases are tuned automatically by Genetic Algorithm (GAs), known as Genetic Fuzzy System (GFS). …”
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  2. 2
    Active force control with iterative learning control algorithm for a vehicle suspension

    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    Published 2013
    “…The simulation was conducted using MATLAB/Simulink software package whilstthe experimental study utilized the existing experimental rig with a hardware-in-the-loop simulation (HILS) configuration with the proposed ILC algorithms incorporated as the new research contribution. …”
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  3. 3
    CONTEXT AWARE TRAFFIC SCHEDULING ALGORITHM FOR COMMUNICATION SYSTEM IN POWER DISTRIBUTION NETWORK

    CONTEXT AWARE TRAFFIC SCHEDULING ALGORITHM FOR COMMUNICATION SYSTEM IN POWER DISTRIBUTION NETWORK by NURSHAZLINA SUHAIMY

    Published 2023
    “…The designated algorithm is tested with Multi-Protocol Label Switching (MPLS) protocol using Network Simulator 3 (NS-3) as a simulation platform. …”
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  4. 4
    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation

    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation by Rong, Li, Shari, Zalina, Ab Kadir, Mohd Zainal Abidin

    Published 2025
    “…A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. …”
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  5. 5
    A novel deadlock detection algorithm for neighbour replication on grid environment

    A novel deadlock detection algorithm for neighbour replication on grid environment by Noriyani, Mohd Zin

    Published 2012
    “…The NRGDD simulation model has been developed to test the algorithm on NRG. …”
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  6. 6
    Design high frequency surgical robot controller: design FPGA-based controller for surgical robot manipulator simscape modeling

    Design high frequency surgical robot controller: design FPGA-based controller for surgical robot manipulator simscape modeling by Taghizadegan, Ali, Piltan, Farzin, Sulaiman, Nasri B.

    Published 2016
    “…To design PID type FPGA-based controller two types algorithm are needed: derivative algorithm and integral algorithm. …”
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  7. 7
    Performance of time based animation of physic motion

    Performance of time based animation of physic motion by Loh, Ngiik Hoon

    Published 2012
    “…It was comprehensible that setting the value of keyframes was unambiguous whHe specifying the timing for keyframe was a harder task and often time consuming. The case study of bouncing balls' simulation was carried out and an algorithm was presented to produce physics-based real time simulation. …”
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  8. 8
    Distributed learning based energy-efficient operations in small cell networks

    Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

    Published 2023
    “…Both user association and power allocation are solved in their respective action spaces. Simulation results demonstrate improved performance in power consumption, load, sum rate, utility, learning rate, convergence, and energy efficiency for small base stations (SBSs) and user equipment (UEs) compared to four benchmarked algorithms, including WMMSE, game theory, Q-learning, and DRL. …”
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  9. 9
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

    Published 2023
    “…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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  10. 10
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

    Published 2023
    “…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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  11. 11
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Soomro, Zubair Adil, Adrianshah, Andi

    Published 2023
    “…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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  12. 12
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

    Published 2023
    “…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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  13. 13
    Hierarchical gaussian reinforcement learning for path planning in uncertain environments

    Hierarchical gaussian reinforcement learning for path planning in uncertain environments by AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw, Akmeliawati, Rini, Shafie, Amir Akramin

    Published 2015
    “…We used Matlab to perform experiments in simulation. The simulation experimental results seem suggest the efficiency of the proposed algorithm in finding optimal paths of autonomous agents.…”
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  14. 14
    Joint optimization of resources allocation for quality of service aware next-generation heterogeneous cellular networks / Hayder Faeq Rasool Alhashimi

    Joint optimization of resources allocation for quality of service aware next-generation heterogeneous cellular networks / Hayder Faeq Rasool Alhashimi by Hayder Faeq Rasool , Alhashimi

    Published 2025
    “…Finally, a State-Action-Reward-State-Action (SARSA) algorithm, which is a reinforcement learning approach, is proposed to solve the power allocation optimization problem. …”
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  15. 15
    Shepherding: An Immune-Inspired Robotics Approach

    Shepherding: An Immune-Inspired Robotics Approach by Razali, Sazalinsyah, Meng, Qinggang, Yang, Shuang-Hua

    Published 2010
    “…The approach uses a memory-based immune network that enhances a robot’s action-selection process and can obtain an overall a quick group response. …”
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  16. 16
    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

    Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Mohamad Hafiz Abu Bakar, Mohamad Hafiz Abu Bakar, Abu Ubaidah Shamsudin, Abu Ubaidah Shamsudin, Ruzairi Abdul Rahim, Ruzairi Abdul Rahim, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

    Published 2023
    “…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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  17. 17
    Resource management in grid computing using ant colony optimization

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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  18. 18
    Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller

    Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller by Ismail, H. Muh Yusuf

    Published 2010
    “…The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. …”
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  19. 19
    Location-Based Approach for Route Maintenance in Dynamic Source Routing Protocol

    Location-Based Approach for Route Maintenance in Dynamic Source Routing Protocol by Sjaugi, Muhammad Farhan

    Published 2008
    “…The objectives of this research are to propose new algorithm to detect route failure as early warning message to the protocol to take further action, and to propose new algorithm for DSR route maintenance to response the early warning message from route failure detection algorithm. …”
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  20. 20
    Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market

    Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market by Kavita Jain, Muhammed Basheer Jasser, Muzaffar Hamzah, Akash Saxena, Ali Wagdy Mohamed

    Published 2022
    “…In this research, we provide HHO-NN (Harris Hawk Optimization-Neural network), a novel algorithm based on Harris Hawk Optimization (HHO) that is capable of fast convergence when compared to previous evolutionary algorithms for automatically searching for meaningful multilayered perceptron neural networks (MPNNs) topologies for optimal bidding. …”
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