Search Results - (("different estimation algorithm") OR ("different optimization algorithm"))

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  1. 1

    A novel Master–Slave optimization algorithm for generating an optimal release policy in case of reservoir operation by Turgut, Mert Sinan, Turgut, Oguz Emrah, Afan, Haitham Abdulmohsin, El-Shafie, Ahmed

    Published 2019
    “…First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. …”
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    Article
  2. 2

    Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

    Published 2019
    “…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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    Article
  3. 3

    Solution of optimal power flow using non-dominated sorting multi-objective based hybrid firefly and particle swarm optimization algorithm by Abdullah Khan, Hizam, Hashim, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi

    Published 2020
    “…Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. …”
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    Article
  4. 4

    Modelling and optimization of assembly line balancing with multi-resource constraints by Ab Rashid, Mohd Fadzil Faisae

    Published 2013
    “…The optimization was conducted by using different optimization algorithms. In SALB, we found that the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) have better performance. …”
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    Research Book Profile
  5. 5

    An optimized multi-layer ensemble framework for sentiment analysis by Lai, Po Hung, Alfred Rayner

    Published 2019
    “…Future works includes testing the framework on different domains of classification and different optimization algorithm.…”
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    Conference or Workshop Item
  6. 6

    Multi-objective constrained algorithm (MCA) and non-dominated sorting genetic algorithm (NSGA-ii) for solving multi-objective crop planning problem by Jarin, Sams, Khatun, Mst Khaleda, Shafie, Amir Akramin

    Published 2016
    “…In this paper, we formulate a crop planning problem as a multiobjective optimization model and try to solve two different versions of the problem using two different optimization algorithms MCA and NSGA. In this two algorithms, they provide superior solutions to maximize total net benefit and minimize total cost. …”
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  7. 7

    Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid by Ku-Mahamud, Ku Ruhana, Ramli, Razamin, Yusof, Yuhanis, Mohamed Din, Aniza, Mahmuddin, Massudi

    Published 2012
    “…Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. …”
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    Monograph
  8. 8

    Modelling of Assembly Sequence Planning Problem using Base Part Concept by Muhamad Arif, Abdullah, M. F. F., Ab. Rashid, Z., Ghazalli, N. M. Z., Nik Mohamed, A. N., Mohd Rose

    Published 2018
    “…Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. Besides the algorithm efficiency, another factor that directly contributed to the quality of the solution is how the problem is modelled. …”
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    Conference or Workshop Item
  9. 9

    Modelling of assembly sequence planning problem using base part concept by M Arif, Abdullah, Mohd Fadzil Faisae, Ab Rashid, Zakri, Ghazalli, Nik Zuki, Nik Mohamed, Ahmad Nasser, Mohd Rose

    Published 2018
    “…Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. Besides the algorithm efficiency, another factor that directly contributed to the quality of the solution is how the problem is modelled. …”
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    Article
  10. 10

    Performance evaluation of PID controller optimisation for wheel mobile robot using Bat based optimisation algorithm by ,, Dwi Pebrianti, Ann Ayop azmi, Nurnajmi Qasrina, Bayuaji, Luhur, Suarin, Nur Aisyah Syafinaz, ,, Muhammad Syafrullah

    Published 2022
    “…In this study, a new proposed hybrid optimization algorithm, called Extended Bat Algorithm (EBA) is used for optimizing the PID controller for the wheel mobile robot. Three different optimization algorithms which are Bat Algorithm (BA), Bat Algorithm with Mutation (BAM) and Extended Bat Algorithm (EBA) are implemented to optimize the value of PID controller gain for wheel mobile robot. …”
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    Book Chapter
  11. 11

    A Review on energy consumption optimization techniques in IoT based smart building environments by Shah, Abdul Salam, Nasir, Haidawati, Fayaz, Muhammad, Lajis, Adidah, Shah, Asadullah

    Published 2019
    “…Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. …”
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    Article
  12. 12

    Enhancing three variants of harmony search algorithm for continuous optimization problems by Alomoush, Alaa A., Alsewari, Abdulrahman A., Kamal Z., Zamli, Alrosan, Ayat, Alomoush, Waleed, Alissa, Khalid

    Published 2021
    “…Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. …”
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    Article
  13. 13

    Accelerated black hole optimization algorithm with enhanced FOPID controller for omni-wheel drive mobile robot system by Basil, Noorulden, Marhoon, Hamzah M., Sahib, Dheyaaldeen Faez, Mohammed, Abdullah Fadhil, Ridha, Hussein Mohammed, Ma’arif, Alfian

    Published 2025
    “…To optimize controller performance, six different optimization algorithms are compared are Accelerated Convergence Black Hole Optimization (ACBHO), Black Hole Optimization (BHO), Aquila Optimizer (AO), Hybrid Firefly Particle Swarm Optimization (HFPSO), Enhanced JAYA (EJAYA), and Sunflower Optimizer (SFO). …”
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    Article
  14. 14

    A review on energy consumption optimization techniques in IoT based smart building environment by Shah, Abdul Salam, Mohamad Nasir, Haidawati, Fayyaz, Muhammad, Lajis, Adidah, Shah, Asadullah

    Published 2019
    “…Researchers have addressed the issue with the help of different optimization algorithm and variations in the parameters to reduce energy consumption. …”
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    Article
  15. 15

    A hybrid bat–swarm algorithm for optimizing dam and reservoir operation by Yaseen, Zaher Mundher, Allawi, Mohammed Falah, Karami, Hojat, Ehteram, Mohammad, Farzin, Saeed, Ahmed, Ali Najah, Koting, Suhana, Mohd, Nuruol Syuhadaa, Jaafar, Wan Zurina Wan, Afan, Haitham Abdulmohsin, El-Shafie, Ahmed

    Published 2019
    “…In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. …”
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    Article
  16. 16

    Modelling and optimization of assembly line balancing with multi-resource constraints by Ab Rashid, Mohd Fadzil Faisae, Razman, Mat Tahar, Nik Mohamed, Nik Mohd Zuki, Rose, Ahmad Nasser Mohd, Baharom, Mohamad Zairi

    Published 2017
    “…The optimization was conducted by using different optimization algorithms. In SALB, we found that the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) have better performance. …”
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    Research Report
  17. 17

    Bayesian Framework based Brain Source Localization Using High SNR EEG Data by Jatoi, M.A., Kamel, N., Gaho, A.A., Dharejo, F.A.

    Published 2019
    “…These sources can be localized using different optimization algorithms. This localization information is usable for diagnoses of brain disorders such as epilepsy, Schizophrenia, depression and Alzheimer. …”
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    Conference or Workshop Item
  18. 18

    Impact of optimizer on the MLP-based models for student performance classification by Osman, Fairul Nazmie, Abdul Aziz, Mohd Azri, Mohd Yassin, Ihsan, Taib, Mohd Nasir

    Published 2025
    “…This study investigates the impact of different optimization algorithms on Multi-Layer Perceptron (MLP) models for student performance forecasting, utilizing a dataset of 99 student samples. …”
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    Article
  19. 19
  20. 20

    Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm by Farzin, Saeed, Singh, Vijay, Karami, Hojat, Farahani, Nazanin, Ehteram, Mohammad, Kisi, Ozgur, Allawi, Mohammed Falah, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

    Published 2018
    “…Seven performance indexes were examined to evaluate the performance of the proposed Muskingum model integrated with IBA, with other models that were also based on the Muskingum Model with three-parameters but utilized different optimization algorithms. The results for the Wilson flood showed that the proposed model could reduce the Sum of Squared Deviations (SSD) value by 89%, 51%, 93%, 69%, and 88%, compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Pattern Search (PS) algorithm, Harmony Search (HS) algorithm, and Honey Bee Mating Optimization (HBMO), respectively. …”
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    Article