Search Results - (( parameter optimisation based algorithm ) OR ( simulation optimization task algorithm ))

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  1. 1
    Completion time driven hyper-heuristic approach for optimisation of scientific workflow scheduling in cloud environment / Ehab Nabiel Mohammad

    Completion time driven hyper-heuristic approach for optimisation of scientific workflow scheduling in cloud environment / Ehab Nabiel Mohammad by Ehab Nabiel , Mohammad

    Published 2018
    “…The performance of the proposed approach is evaluated by comparing it with four population-based approaches and an existing hyper-heuristic approach named Hyper-Heuristic Scheduling Algorithm (HHSA). …”
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    Thesis
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  2. 2
    Optimised multi-robot path planning via smooth trajectory generation

    Optimised multi-robot path planning via smooth trajectory generation by Loke, Zhi Yu

    Published 2024
    “…Particle swarm optimization (PSO) outperforms conventional methods like artificial potential fields (APF), the Dijkstra algorithm, and the A* algorithm in path planning for mobile robots. …”
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    Final Year Project / Dissertation / Thesis
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  3. 3
    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm

    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm by Muhammad Syahril Mohamad Sainal

    Published 2022
    “…Meta-heuristic algorithms are mostly used to solve this problem. For example, Genetic Algorithm with Particle Swarm Optimization, Genetic Algorithm with Artificial Bee Colony Algorithms (ABC) and Genetic Algorithm with Ant Colony Optimization Algorithms. …”
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    Academic Exercise
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  4. 4
    Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm

    Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm by Nik Mohamed Hazli, Nik Muhammad Aiman

    Published 2018
    “…Optimisation is difficult to optimise as there are three parameters that need to be tuned, Kp, Integral parameter, Ki, and derivative parameter, Kd. …”
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    Monograph
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  5. 5
    Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application

    Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application by Hardhienata, Medria Kusuma Dewi, Priandana, Karlisa, Putra, Daffa Rangga, Sriatun, Mamiek, Wulandari, Buono, Agus, Mohamed, Raihani

    Published 2024
    “…Simulation results showed that the proposed ACO algorithm with the modified efficiency factor improved the performance of basic ACO algorithm for solving task allocation problem in terms of the average total travel cost for each agent. …”
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  6. 6
    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. …”
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  7. 7
    OTS: an optimal tasks scheduling algorithm based on QoS in cloud computing network

    OTS: an optimal tasks scheduling algorithm based on QoS in cloud computing network by Alhakimi, Mohammed Ameen, Latip, Rohaya

    Published 2019
    “…This study presents an optimal tasks scheduling algorithm by enhancing Max-Min algorithm. …”
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  8. 8
    Task scheduling in cloud computing using Harris-Hawk Optimization

    Task scheduling in cloud computing using Harris-Hawk Optimization by Iza A. A. Bahar, Azali Saudi, Abdul Kadir, Syed Nasirin Syed Zainol Abidin, Tamrin Amboala, Esmadi Abu Bin Abu, Abdullah B. Mohd. Tahir, Suddin Lada

    Published 2024
    “…This paper presents a simulation of the Harris-Hawk Optimization (HHO) algorithm, which aims to minimize the makes pan of a specified task set in a cloud computing environment. …”
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    Proceedings
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  9. 9
    Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter

    Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
    “…This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine.A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables.The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM.Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. …”
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  10. 10
    Using Electromagnetism-like algorithm and genetic algorithm to optimize time of task scheduling for dual manipulators

    Using Electromagnetism-like algorithm and genetic algorithm to optimize time of task scheduling for dual manipulators by Abed I.A., Sahari K.S.M., Koh S.P., Tiong S.K., Jagadeesh P.

    Published 2023
    “…A method based on Electromagnetism-Like algorithm (EM) and Genetic Algorithm (GA) is proposed to determine the time-optimal task scheduling for dual robot manipulators. …”
    Conference paper
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  11. 11
    An evaluation of network load balancing through Ant Colony Optimization (ACO) based technique / Muhammad Nur Zikri Mohamad Hafizan

    An evaluation of network load balancing through Ant Colony Optimization (ACO) based technique / Muhammad Nur Zikri Mohamad Hafizan by Mohamad Hafizan, Muhammad Nur Zikri

    Published 2020
    “…The results also show that the ACO algorithm was able to outperform the Randomized and Round Robin algorithm in all simulation configurations.…”
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    Student Project
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  12. 12
    Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment

    Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment by Gabi, Danlami, Ismail, Abdul Samad, Zainal, Anazida, Zakaria, Zalmiyah, Al-Khasawneh, Ahmad

    Published 2018
    “…In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. …”
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  13. 13
    An optimal tasks scheduling algorithm based on QoS in cloud computing network

    An optimal tasks scheduling algorithm based on QoS in cloud computing network by Alhakimi, Mohammed Ameen Mohammed Abdo

    Published 2017
    “…This study presents an optimal task scheduling algorithm by enhancing Max-Min and TS algorithm. …”
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    Thesis
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  14. 14
    Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots

    Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots by Hanafi Ahmad Hijazi, Patricia Anthony

    Published 2006
    “…This research explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis tasks while avoiding obstacles in a simulated 30 physics environment, to overcome problems involving more than one objective, where these objectives usually trade-off among each other and are expressed in different units. …”
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    Research Report
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  15. 15
    A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application

    A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application by Zainal Abidin, Zulkifli

    Published 2011
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  16. 16
    An enhanced discrete symbiotic organism search algorithm for optimal task scheduling in the cloud

    An enhanced discrete symbiotic organism search algorithm for optimal task scheduling in the cloud by Sa’ad, Suleiman, Muhammed, Abdullah, Abdullahi, Mohammed, Abdullah, Azizol, Ayob, Fahrul Hakim

    Published 2021
    “…Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. …”
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  17. 17
    Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing

    Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing by Saif, Faten A., Latip, Rohaya, Hanapi, Zurina Mohd, Shafinah, Kamarudin

    Published 2023
    “…The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.…”
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  18. 18
    Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems

    Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems by Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Mohd Saberi, Mohamad, Watada, Junzo

    Published 2016
    “…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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  19. 19
    Single-solution Simulated Kalman Filter algorithm for global optimisation problems

    Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

    Published 2018
    “…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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  20. 20
    Single-solution Simulated Kalman Filter algorithm for global optimisation problems

    Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

    Published 2018
    “…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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    Article
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