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  1. 1
    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments

    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments by Khaksar, Weria

    Published 2013
    “…Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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  2. 2
    Minimizing machining airtime motion with an ant colony algorithm

    Minimizing machining airtime motion with an ant colony algorithm by Abdullah, Haslina, Ramli, Rizauddin, Abd Wahab, Dzuraidah, Abu Qudeiri, Jaber

    Published 2016
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  3. 3
    Mobile robot navigation using GPS to solve target location achieving problem / Saiful Zaimy Yahaya, Rozan Boudville and Mohamad Faizal Abdul Rahman

    Mobile robot navigation using GPS to solve target location achieving problem / Saiful Zaimy Yahaya, Rozan Boudville and Mohamad Faizal Abdul Rahman by Yahaya, Saiful Zaimy, Boudville, Rozan, Abdul Rahman, Mohamad Faizal

    Published 2010
    “…The path finding algorithm will basically finds the best path and instructs the robot to moves to its location by keep on refreshing its positions. …”
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  4. 4
    A Fuzzy Logic Positional-Based Controller For Sensorbased Robotic Motion Planning

    A Fuzzy Logic Positional-Based Controller For Sensorbased Robotic Motion Planning by Khaksar W., Yousefi M., Saharia K.S.M., Ismail F.B.

    Published 2023
    “…Despite the large volume of research in this field, the computational complexity of the motion planning and the increasing potential application domains require more accurate and efficient motion planning algorithms. …”
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  5. 5
    A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains

    A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains by Khaksar W., Sahari K.S.M.

    Published 2023
    “…Sensor-based motion planning is one the most challenging tasks in robotics where various approaches and algorithms have been proposed to achieve different planning goals. …”
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  6. 6
    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system by Khaksar, Weria, Tang, Sai Hong, Mohamed Sahari, Khairul Salleh, Khaksar, Mansoor, Toressen, Jim

    Published 2019
    “…The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. …”
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  7. 7
    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system by Khaksar W., Hong T.S., Sahari K.S.M., Khaksar M., Torresen J.

    Published 2023
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  8. 8
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…The simulation is implemented with iFogSim and java programming language. …”
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  9. 9
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…Global pheromone update is performed after the completion of 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 existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  10. 10
    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
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  11. 11
    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…This thesis presents a new approach to optimize the performance of a dual beam optical scanning system in terms of its scanning combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the scanning tasks need to be segregated and assigned for each scanner head, and path planning where the best combinatorial paths for each scanner are determined in order to minimize the total motion of scanning time. …”
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  12. 12
    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module by Koh J.S.P., Aris I.B., Ramachandaramurthy V.K., Bashi S.M., Marhaban M.H.

    Published 2023
    “…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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  13. 13
    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module by Koh J.S.P., Aris I.B., Ramachandaramurthy V.K., Bashi S.M., Marhaban M.H.

    Published 2023
    “…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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  14. 14
    Development of a motion planning and obstacle avoidance algorithm using adaptive neuro fuzzy inference system for mobile robot navigation

    Development of a motion planning and obstacle avoidance algorithm using adaptive neuro fuzzy inference system for mobile robot navigation by Muslim, Farah Kamil Abid

    Published 2017
    “…This would allow the FLC to generate a local path for an obstacle avoidance system unique to mobile robot navigation in dynamic environments. …”
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  15. 15
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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  16. 16
    A coverage path planning approach for autonomous radiation mapping with a mobile robot

    A coverage path planning approach for autonomous radiation mapping with a mobile robot by Abd Rahman N.A., Sahari K.S.M., Hamid N.A., Hou Y.C.

    Published 2023
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  17. 17
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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  18. 18
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  19. 19
    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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  20. 20
    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module by Koh, Johnny Siaw Paw, Aris, Ishak, Ramachandaramurthy, Vigna Kumaran, Bashi, Sinan Mahmod, Marhaban, Mohammad Hamiruce

    Published 2006
    “…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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