Search Results - (( parallel distribution factor algorithm ) OR ( pareto distribution system algorithm ))

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  1. 1

    A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution by Warid, Warid, Hizam, Hashim, Mariun, Norman, Abdul Wahab, Noor Izzri

    Published 2018
    “…Simulation results reveal the proposed algorithm’s ability to produce real and well-distributed Pareto optimum fronts for all considered multi-objective optimization cases. …”
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    Article
  2. 2

    Multi-objective spiral dynamic algorithms-based for a better accuracy and diversity by Ahmad Azwan, Abdul Razak

    Published 2019
    “…The produced Pareto front curve is a measure of how good the solution produced by the algorithm is. …”
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    Thesis
  3. 3

    Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system by Ridha, Hussein Mohammed, Gomes, Chandima, Hizam, Hashim, Mirjalili, Seyedali

    Published 2020
    “…The third scenario outperforms other scenarios in terms of coverage and convergence of the distribution of solution to the Pareto front. As a conclusion, The MS-MOSS algorithm is found to be very effective in sizing of SAPV system.…”
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    Article
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    Pareto optimal approach in Multi-Objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) for optimal Distributed Generation Photovoltaic (DGPV) integration in power sy... by Syed Mustaffa S.A., Musirin I., Mohamad Zamani M.K., Othman M.M.

    Published 2023
    “…Computer programming; Distributed power generation; Evolutionary algorithms; Integration; Pareto principle; Renewable energy resources; Voltage control; Fast voltage stability indices; Multi objective; Optimal; Pareto-optimal; Photovoltaic; Distributed computer systems…”
    Article
  6. 6

    Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation by Mahesh, K., Nallagownden, P., Elamvazuthi, I.

    Published 2016
    “…This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. …”
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    Article
  7. 7

    Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation by Mahesh, K., Nallagownden, P., Elamvazuthi, I.

    Published 2016
    “…This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. …”
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    Article
  8. 8

    NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment by Shen, jiazheng, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan, Wang, Xinming

    Published 2024
    “…By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. …”
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    Article
  9. 9

    Optimal placement and sizing of renewable distributed generations and capacitor banks into radial distribution systems by Mahesh, K., Nallagownden, P., Elamvazuthi, I.

    Published 2017
    “…In recent years, renewable types of distributed generation in the distribution system have been much appreciated due to their enormous technical and environmental advantages. …”
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    Article
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    New Hybrid Multi-Objective Optimization Technique for Multi-DG Installation in Bulk Distribution System by Abdullah A., Musirin I., Othman M.M., Rahim S.R.A., Kumar A.V.S.

    Published 2025
    “…This article proposes a multi-objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm to identify the optimal sizing and placement of distribution generation (DG) in a radial distribution system (RDS). …”
    Conference paper
  12. 12

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”
    Article
  13. 13
  14. 14

    Optimal Integration of Active and Reactive Power DGs in Distribution Network via a Novel Multi-Objective Intelligent Technique by Abdullah A., Musirin I., Othman M.M., Rahim S.R.A., Shaaya S.A., Senthil Kumar A.V.

    Published 2025
    “…This work introduces a novel approach called the Multi-Objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm. This algorithm aims to determine the optimal sizes and positions for Type III distributed generators (DGs) that generate both active and reactive power. …”
    Conference paper
  15. 15

    Non-dominated sorting manta ray foraging algorithm with an application to optimize PD control by Abdul Razak, Ahmad Azwan, Nasir, Ahmad Nor Kasruddin, Abd Ghani, N. M., Mohammad, Shuhairie, Mat Jusof, Mohd Falfazli, Mhd Rizal, Nurul Amira

    Published 2022
    “…Meanwhile, CD is a strategy to preserve good distribution of solutions along the PF. This proposed algorithm is called NSMRFO. …”
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    Conference or Workshop Item
  16. 16

    Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes by Mohammad Sigit Arifianto, Tze, Kenneth Kin Teo, Liau, Chung Fan, Liawas Barukang, Zaturrawiah Ali Omar

    Published 2007
    “…Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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    Research Report
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    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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    Article
  19. 19

    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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    Article
  20. 20

    Multiobjective optimization using particle swarm optimization with non-Gaussian random generators by Ganesan, T., Vasant, P., Elamvazuthi, I.

    Published 2016
    “…The stochastic engines operate using the Weibull distribution, Gamma distribution, Gaussian distribution and a chaotic mechanism. …”
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    Article