Search Results - (( wolf optimization path algorithm ) OR ( based operations bayes algorithm ))*

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  1. 1

    Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking by Shen, Jiazheng, Hong, Tang Sai, Fan, Luxin, Zhao, Ruixin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan

    Published 2024
    “…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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    Enhancing performance of global path planning for mobile robot through Alpha–Beta Guided Particle Swarm Optimization (ABGPSO) algorithm by Ahmad, Javed, Ab Wahab, Mohd Nadhir, Ramli, Ahmad, Misro, Md Yushalify, Ezza, Wan Zafira, Wan Hasan, Wan Zuha

    Published 2025
    “…Through extensive simulations across various static environment maps, we demonstrate that the ABGPSO algorithm outperforms existing state-of-the-art optimization techniques, including Genetic Algorithms (GA), Grey Wolf Optimization (GWO), and modern optimizers like the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO) and Reptile search algorithm (RSA). …”
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    Continuous path planning of Kinematically redundant manipulator using Particle Swarm Optimization by Machmudah, A., Parman, S., Baharom, M.B.

    Published 2018
    “…Based on a geometrical analysis, feasible postures of a self-motion are mapped into an interval so that there will be an angle domain boundary and a redundancy resolution to track the desired path lies within this boundary. To choose a best solution among many possible solutions, meta-heuristic optimizations, namely, a Genetic Algorithm (GA), a Particle Swarm Optimization (PSO), and a Grey Wolf Optimizer (GWO) will be employed with an optimization objective to minimize a joint angle travelling distance. …”
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  5. 5

    Continuous path planning of Kinematically redundant manipulator using Particle Swarm Optimization by Machmudah, A., Parman, S., Baharom, M.B.

    Published 2018
    “…Based on a geometrical analysis, feasible postures of a self-motion are mapped into an interval so that there will be an angle domain boundary and a redundancy resolution to track the desired path lies within this boundary. To choose a best solution among many possible solutions, meta-heuristic optimizations, namely, a Genetic Algorithm (GA), a Particle Swarm Optimization (PSO), and a Grey Wolf Optimizer (GWO) will be employed with an optimization objective to minimize a joint angle travelling distance. …”
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  6. 6

    A Hybrid Metaheuristic Technique Based on Grey Wolf Optimisation, Symbiotic Organism Search, and Ant Colony Optimisation for Solving Multi-Objective Vehicle Routing Problems by Ary, Maxsi

    Published 2025
    “…This study developed a hybrid metaheuristic algorithm involving grey wolf optimization, symbiotic organism search, and ant colony optimization (HMGSA) to address the multi-objective VRP. …”
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    Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

    Published 2024
    “…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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    Performance evaluation for different intrusion detection system algorithms using machine learning by Zarir, Mustafa Nadhim

    Published 2018
    “…The objectives of this project is to evaluate the performance of various intrusion detection algorithms based on machine learning. The algorithms considered are the Naive Bays Algorithm, Decision Tree Algorithm and Hybrid Algorithm for different datasets. …”
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    The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification by Mokhairi, Makhtar, Engku Fadzli Hasan, Syed Abdullah, Fatma Susilawati, Mohamad

    Published 2015
    “…It also target to study the effect of morphological operation and feature selection to the accuracy. For the classification experiment, it was tested using four types of classifiers: BayesNet, NaiveBayesUpdateable, RandomTree and IBk.…”
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    An enhanced feature selection technique for classification of group based holy Quran verses by Abdullahi Oyekunle, Adeleke

    Published 2018
    “…The proposed FS technique is a combination of filter-based information gain (IG) and wrapper-based CFS algorithms. …”
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    An enhanced feature selection technique for classification of group-based holy quran verses by Oyekunle, Adeleke Abdullahi

    Published 2018
    “…The proposed FS technique is a combination of JUter-based information gain (JG) and wrapper-based CFS algorithms. …”
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    Android malware detection using permission based static analysis by Mohd Ariffin, Noor Afiza, Casinto, Hanna Pungo

    Published 2024
    “…Then, K-nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms are applied, and the proposed method is compared with the previous studies and the expected experimental results of the proposed approach will be higher.…”
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    Android malware detection using permission based static analysis by Mohd Ariffin, Noor Afiza, Casinto, Hanna Pungo

    Published 2023
    “…Then, K-nearest Neighbor (KNN) and Na¯ve Bayes (NB) algorithms are applied, and the proposed method is compared with the previous studies and the expected experimental results of the proposed approach will be higher.…”
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    Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

    Published 2020
    “…Therefore, four supervised machine learning classification algorithms such as Multinomial Naïve Bayes, Support Vector Machine, Decision Trees, and kNearest Neighbors were investigated. …”
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    Classification of polymorphic virus based on integrated features by A Hamid, Isredza Rahmi, Subramaniam, Sharmila, Sutoyo, Edi, Abdullah, Zubaile

    Published 2018
    “…We spilt the dataset based on 60% for training and 40% for testing. The performance metric of accuracy value, receiver operating characteristic and mean absolute error are compared between two algorithms in the experiment of static, dynamic and integrated features. …”
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