Search Results - (( _ distribution swarm algorithm ) OR ( peer evaluation ((new algorithm) OR (based algorithm)) ))

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  1. 1

    Levy tunicate swarm algorithm for solving numerical and real-world optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

    Published 2022
    “…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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    Conference or Workshop Item
  2. 2

    Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation by Khan, Abdullah

    Published 2022
    “…Finally, a crowding distance and non-dominated-sorting-based multi-objective hybrid firefly & particle swarm optimization (MOHFPSO) algorithm is designed for MOOPF problems. …”
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    Thesis
  3. 3

    Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing by Parmesivan, Yuganes, Hasan, Sazlinah, Muhammed, Abdullah

    Published 2018
    “…Cloud computing has become biggest buzz in the computer era these days.It runs entire operating systems on the cloud and doeverything on cloud to store data off-site.Cloud computing is primarily based on grid computing, but it’s a new computational model.Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services.The primary substance of cloud computing is to deal the computing power,storage,different sort of stages and services which assigned tothe external users on demand through the internet.Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing.In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example,when cloud clients try to access and send request tothe same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers.Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded.A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time.The proposed hybrid algorithm has been experimented by using CloudSim simulator.The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm.…”
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    Article
  4. 4

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

    Published 2011
    “…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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    Thesis
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    Electricity distribution network for low and medium voltages based on evolutionary approach optimization by Hasan, Ihsan Jabbar

    Published 2015
    “…This thesis proposes an algorithm to find the optimum distribution substation placement and sizing by utilizing the PSO algorithm and optimum feeder routing using modified Minimum Spanning Tree (MST). …”
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    Thesis
  7. 7

    An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization by Majid, Mad Helmi Ab.

    Published 2019
    “…Searching a source with a complex spatial distribution pattern is one of the possible swarm robotics tasks. …”
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    Thesis
  8. 8

    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

    Published 2019
    “…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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    Thesis
  9. 9

    Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture by Ibrahim, Abeer A.Z., Hashim, Fazirulhisyam, Sali, Aduwati, Noordin, Nor K., Navaie, Keivan, Fadul, Saber M.E.

    Published 2023
    “…A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. …”
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    Article
  10. 10

    Task scheduling on computational grids using Gravitational Search Algorithm by Zarrabi, Amirreza, Samsudin, Khairulmizam

    Published 2014
    “…We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. …”
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    Article
  11. 11

    Optimal Location And Sizing Of Distrubuted Generator Using PSO And GA Algorithms In Power Systems by Hassan, Ayat Saleh

    Published 2019
    “…This research aimed to reduce total power losses and improve voltage profiles of the distribution system by proposing a practical swarm optimizion algorithm GA genetic algorithm to optimize DG size and location by taking into consideration increase number of DG units in the system. …”
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    Thesis
  12. 12

    A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems by Joe Henry Obit, Rayner Alfred, Mansour Hassani Abdalla

    Published 2017
    “…When coupled with two, four and six agents, the Asynchronous Cooperative Distributed Hyper-heuristic (ACDHH) algorithm is able to improve the solution quality for a large instance.…”
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    Article
  13. 13

    Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari by Mohammed , Heydari

    Published 2017
    “…An improved particle swarm algorithm (HPSOGA) is used to solve complex problems of water resources optimization. …”
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    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman by Azman, Alya Kauthar

    Published 2025
    “…This study applies Genetic Algorithms (GA) to optimize space distribution for test scheduling, addressing the challenge of managing multiple test sessions across distinct locations. …”
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    Thesis
  16. 16

    Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method by Lawal, Sani Mohammed

    Published 2012
    “…The proposed PSO algorithm is used to determine optimal placement and size of DG in radial distribution networks, where Forward Backward Sweep Method (FBSM) of distribution load flow analysis was used, to determine the actual power loss in the system. …”
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    Thesis
  17. 17

    An improved bat algorithm with artificial neural networks for classification problems by Rehman Gillani, Syed Muhammad Zubair

    Published 2016
    “…Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. …”
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  18. 18

    Self-organized HGBBDSA approach for the power allocation in OFDMA-based heterogeneous network by Mohammad Kamrul, Hasan, Ismail, Ahmad Fadzil, Islam, Shayla, Wahidah, Hashim

    Published 2016
    “…The integration of GA with Biogeography Based Optimization algorithm benchmarked over the Particle Swarm Optimization (PSO). …”
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    Article
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    Energy efficient cluster head distribution in wireless sensor networks by Siew, Zhan Wei

    Published 2013
    “…In order to maximize the network lifetime, fuzzy logic CH selection (FLCH) and particle swarm optimisation (PSO) are embedded in LEACH protocol for better CHs distribution and hence prolong the network lifetime. …”
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    Thesis