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  1. 1
    Search algorithms for path planning problems in harsh wireless sensor network environment

    Search algorithms for path planning problems in harsh wireless sensor network environment by Hong, Siaw Swin

    Published 2017
    “…Each algorithm solves its respective problem with Enhanced D* solving long initial delay in dynamic environment, NBN solves harsh conditions with limited computational power, and FWN optimized the result of TSP path planning problem with minimal computational effort. …”
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  2. 2
    An Integrated RRT*SMART-A* Algorithm for solving the Global Path Planning Problem in a Static Environment

    An Integrated RRT*SMART-A* Algorithm for solving the Global Path Planning Problem in a Static Environment by Suwoyo, Heru, Adriansyah, Andi, Andika, Julpri, Ubaidillah, Abu

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  3. 3
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by SUWOYO, HERU, ADRIANSYAH, ANDI, ANDIKA, JULFRI, SHAMSUDIN, ABU UBAIDAH, ZAKARIA, MOHAMAD FAUZI

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  4. 4
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by Suwayo, Heri, Adrishah, Andi, Andika, Juleri, Shamdudin, Abu Ubaidah, Zakaria, Mohamad Fauzi

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  5. 5
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by SUWOYO, HERU, ADRIANSHAH, ANDI, ANDIKA, JULPRI, SHAMSUDIN, ABU UBAIDAH, ZAKARIA, MOHAMAD FAUZI

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  6. 6
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by HERU SUWOYO, HERU SUWOYO, ANDI ADRIANSYAH, ANDI ADRIANSYAH, JULPRI ANDIKA, JULPRI ANDIKA, SHAMSUDIN, A LT UBAIDAH, ZAKARIA, MOHAMAD FAUZI

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  7. 7
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by Heru Sowoyo, Heru Sowoyo, Andi Adrianshah, Andi Adrianshah, Julpri Andika, Julpri Andika, Shamsuddin, Abu Ubaidah, Zakaria, Mohamad Fauzi

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  8. 8
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by SUWOYO, HERU, ADRIANSYAH, ANDI, JULI RI ANDIKA, JULI RI ANDIKA, SHAMSUDIN, ABU UBAIDAH, ZAKARIA, MOHAMAD FAUZI

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  9. 9
    Path planning algorithm for a car like robot based on MILP method

    Path planning algorithm for a car like robot based on MILP method by Mohd Sadri, Mohd Pawzi

    Published 2013
    “…This algorithm provides the robot the possibility to move from the initial position to the final position (target). …”
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  10. 10
    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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  11. 11
    Development of an improved Jellyfish Search (JS) algorithm for solving the optimal path problem of multi-robot collaborative multi-tasking in complex vertical farms

    Development of an improved Jellyfish Search (JS) algorithm for solving the optimal path problem of multi-robot collaborative multi-tasking in complex vertical farms by Shen, Jiazheng, Tang, Saihong, Zhao, Ruixin, Fan, Luxin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan

    Published 2025
    “…The VFSMRCTP model is solved using the TLDW-JS algorithm and a number of comparison algorithms in order to analyze the algorithm’s performance. …”
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  12. 12
    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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  13. 13
    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
    “…The motion planning problem poses the question of how a robot can move from an initial to a final position. Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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  14. 14
    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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  15. 15
    Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system

    Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system by Mohammad Ata, Karimeh Ibrahim, Che Soh, Azura, Ishak, Asnor Juraiza, Jaafar, Haslina

    Published 2021
    “…A common algorithm to solve the single-source shortest path (SSSP) is the Dijkstra algorithm. …”
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  16. 16
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by SUW, HERU, MI ATILTIANSYAH, AN, ANDIKA, JULI RI, SHAVISUDIN, UBAIDAH, AUZI ZAICARIX, M °H MAD

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  17. 17
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by SUW, HERU, AtiltIANSYAH, AN- MI, RI ANDIKA, JULI, SHAVISUDIN, UBAIDAH, AUZI ZAICARIX, M °H MAD

    Published 2023
    “…Thus, RRT*-Smart, a further development of RRT*, is considered ideal for solving RRT* problems. Unlike RRT*, RRT*-Smart applies a path optimization by removing the redundant nodes from the initial path when it is gained. …”
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  18. 18
    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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  19. 19
    Solving the optimal path planning of a mobile robot using improved Q-learning

    Solving the optimal path planning of a mobile robot using improved Q-learning by Low, Ee Soong, Ong, Pauline, Cheah, Kah Chun

    Published 2019
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  20. 20
    Performance comparison between genetic algorithm and ant colony optimization algorithm for mobile robot path planning in global static environment / Nohaidda Sariff

    Performance comparison between genetic algorithm and ant colony optimization algorithm for mobile robot path planning in global static environment / Nohaidda Sariff by Sariff, Nohaidda

    Published 2011
    “…The objective is to verify and compare the effectiveness of both algorithms in finding the optimal robot path in different types of global map environments. …”
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