Search Results - (( _ distribution swarm algorithm ) OR ( user evaluation ((new algorithm) OR (bat algorithm)) ))

Refine Results
  1. 1
  2. 2

    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.…”
    Get full text
    Get full text
    Get full text
    Article
  3. 3

    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2017
    “…In terms of number of users in the cell, the algorithm could achieve up to 240% of maximum throughput, 61% reduction in ECR and 150% improvement in EE. …”
    Get full text
    Get full text
    Thesis
  4. 4

    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm by Muhammad Syahril Mohamad Sainal

    Published 2022
    “…This research will conduct comparison of hybrid Genetic Algorithm and Bat Algorithm (GA-BA) with Genetic Algorithm (GA) and Bat Algorithm (BA). …”
    Get full text
    Get full text
    Get full text
    Academic Exercise
  5. 5
  6. 6

    An empirical study of density and distribution functions for ant swarm optimized rough reducts by Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah

    Published 2011
    “…Ant colony approach in Ant Swarm algorithm generates local solutions which satisfy the Gaussian distribution for global optimization using PSO algorithm. …”
    Get full text
    Get full text
    Get full text
    Book Chapter
  7. 7

    AN INVESTIGATION ON PARTICLE SWARM OPTIMIZATION ALGORITHM FOR DISTRIBUTION SYSTEM PLANNING WITH DISTRIBUTED GENERATION by Shamshiri, Meysam, Gan, Chin Kim, Hasan, I. J., Ab Ghani, Mohd Ruddin

    Published 2012
    “…In this regards, this paper presents the Particle Swarm Optimization (PSO) algorithm to solve the distribution planning problem with DG. …”
    Get full text
    Get full text
    Conference or Workshop Item
  8. 8

    AN INVESTIGATION ON PARTICLE SWARM OPTIMIZATION ALGORITHM FOR DISTRIBUTION SYSTEM PLANNING WITH DISTRIBUTED GENERATION by Shamshiri, Meysam, Gan, Chin Kim, Hassan, I. J., Ab Ghani, Mohd Ruddin

    Published 2012
    “…In this regards, this paper presents the Particle Swarm Optimization (PSO) algorithm to solve the distribution planning problem with DG. …”
    Get full text
    Get full text
    Conference or Workshop Item
  9. 9
  10. 10

    Using particle swarm optimization algorithm in the distribution system planning by Shamshiri, Meysam, Gan, Chin Kim, Jusoff, Kamaruzaman, Hasan, Ihsan Jabbar, Ab Ghani, Mohd Ruddin, Yusoff, Mariana

    Published 2013
    “…This paper utilizes the Particle Swarm Optimization (PSO) algorithm to solve the distribution planning problem with DG. …”
    Get full text
    Get full text
    Get full text
    Article
  11. 11

    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. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  12. 12

    Optimal multiple distributed generation output through rank evolutionary particle swarm optimization by Jamian, J.J., Mustafa, M.W., Mokhlis, Hazlie

    Published 2015
    “…Moreover, the local best (P-best) and global best (G(best)) values are obtained in simplify manner in the REPSO algorithm. The performance of this new algorithm will be compared to 3 well-known PSO methods, which are Conventional Particle Swarm Optimization (CPSO), Inertia Weight Particle Swarm Optimization (IWPSO), and Iteration Particle Swarm Optimization (IPSO) on 10 mathematical benchmark functions, and solving the optimal DG output problem. …”
    Get full text
    Get full text
    Get full text
    Article
  13. 13

    Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems by Mohammed Saffer, Ihsan Salman, Khalid, Salama A. Mostafa, Hayder H. Safi, Ahmad Khalaf, Bashar

    Published 2023
    “…This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  14. 14

    Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems by Salman, Ihsan, Mohammed Saffer, Khalid, H. Saf, Hayder, A. Mostafa, Salama, Khalaf, Bashar Ahmad

    Published 2023
    “…This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15

    Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems by Salman, Ihsan, Mohammed Saffer, Khalid, Salama A. Mostafa, Hayder H. Safi, Ahmad Khalaf, Bashar

    Published 2023
    “…This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  16. 16

    Using Particle Swarm Optimization Algorithm in the Distribution System Planning by Shamshiri, Meysam, Gan, Chin Kim, Mariana, Yusoff, Ab Ghani, Mohd Ruddin

    Published 2013
    “…This paper utilizes the Particle Swarm Optimization (PSO) algorithm to solve the distribution planning problem with DG. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption by Alsammak I.L.H., Mahmoud M.A., Gunasekaran S.S., Ahmed A.N., Alkilabi M.

    Published 2024
    “…To achieve this goal, we used the improved random walk algorithm to explore the distributed fire spots and a self-coordination mechanism based on the stigmergy as an indirect communication between the swarm drones, taking into account the collision avoidance factor, the amount of extinguishing fluid, and the flight range of the drones. …”
    Article
  18. 18
  19. 19
  20. 20