A study on modified PSO algorithm in cloud computing

Ngatman, Mohd Farhan; Sharif, Johan Mohamad; Ngadi, Md. Asri

doi:10.1109/ict-ispc.2017.8075341

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…To obtain the novel P best value, the arithmetic average is applied to the past iteration and stores the P best value. As a result, the same algorithm is used to calculate G best and velocity updates [27].…”

Section: Modifed Particle Swarm Optimization (Mpso)mentioning

confidence: 99%

“…Te researchers have developed many soft computing strategies in the literature to reduce the harmonics in the grid-connected matrix converters. For optimizing the cost function in soft computing, numerous optimization techniques are suggested, such as PSO [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], fuzzy logic [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], neuro-fuzzy [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], genetic algorithm …”

Section: Introductionmentioning

confidence: 99%

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Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System

Amarendra

Pandian

Reddy³

et al. 2023

International Transactions on Electrical Energy Systems

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Advanced power converters are being developed due to the latest developments in power electronics switches. The matrix converter (MC) assessment is the product of this drastic growth. MC is a bidirectional power flow device with a single-step energy conversion mechanism that operates at variable voltage and frequency. The main drawback of MC is the presence of harmonics due to power electronic device switching. A lot of research has been done to decrease the harmonics content present in the MC, but it is restricted to a certain level only. To overcome this limit, the harmonics are minimized in the MC by incorporating soft computing controllers. This article presents particle swarm optimization (PSO), modified PSO (MPSO), and modified hybrid PSO (MHPSO) based controllers for different loads. The proposed MC with a soft computing controller reduces the harmonics by selecting suitable switching pulses for every sample. The simulations are made using the MATLAB/Simulink environment. The results of the proposed MHPSO controller reduce the THD value from 8.654 with a PSO approach to 7.536 in the presence of nonlinear loads.

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Section: Modifed Particle Swarm Optimization (Mpso)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System

Amarendra

Pandian

Reddy³

et al. 2023

International Transactions on Electrical Energy Systems

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“…Ngatman et al [9] survey on modified PSO compared to traditional PSO to solve issues of random initialization of population for convergence of best solution by exploring the search space effectively. This survey is useful for many authors to propose the advancement of PSO by considering the study.…”

Section: Alsaidy Et Al [2] Proposed Heuristic Initialized Pso [3] [4]mentioning

confidence: 99%

Multi-objective based Cloud Task Scheduling Model with Improved Particle Swarm Optimization

Udatha¹,

Lakshmeeswari²

2021

IJACSA

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Now-a-days, advanced technologies have emerged from the parallel, cluster, client-server, distributed, and grid computing paradigms. Cloud is one of the advanced technology paradigms that deliver services to users on demand by cost per usage over the internet. Nowadays, a number of cloud services have rapidly increased to facilitate the user requirements. The cloud is able to provide anything as a service over web networks from hardware to applications on demand. Due to the complex infrastructure of the cloud, it needs to manage resources efficiently, and constant monitoring is required from time to time. Task scheduling plays an integral role in improving cloud performance by reducing the number of resources used and efficiently allocating tasks to the requested resources. The paper's main idea attempts to assign and schedule the resources efficiently in the cloud environment by using proposed Multi-Objective based Hybrid Initialization of Particle Swarm Optimization (MOHIPSO) strategy by considering both sides of the cloud vendor and user. The proposed algorithm is a novel hybrid approach for initializing particles in PSO instead of random values. This strategy can obtain the minimum total task execution time for the benefit of the cloud user and maximum resource usage for the benefit of the cloud provider. The proposed strategy shows improvement over standard PSO and the other heuristic initialization of PSO approach to reduce the makespan, execution time, waiting time, and virtual machine imbalance parameters are considered for comparison results.

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“…Combining the PSO algorithm with the SA algorithm can improve convergence rate and guarantee an accurate solution. This combination is called particle swarm-simulated annealing (SA_PSO) [29]. The SA_PSO reduces the tasks average running time and raises the availability rate of resources.…”

Section: Task Scheduling In Cloud Computing Environmentmentioning

confidence: 99%

“…As PSO algorithms suffer from low performance with a large scale of data [23], many algorithms [24]- [31] have been proposed to improve the performance of PSO. These algorithms include the modified-PSO algorithms [24]- [28] and the hybrid-PSO algorithms [29]- [31] which try to enhance the makespan and standard deviation for scheduling a small number of tasks. However, based on our survey, no much work has been done to improve the scheduling performance with large-scale allocation in a cloud environment (a maximum of 1000 tasks in [27]).…”

Section: Task Scheduling In Cloud Computing Environmentmentioning

confidence: 99%

IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

et al. 2019

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Task scheduling is one of the essential techniques in the cloud computing environment. It is required for allocating tasks to the proper resources and optimizing the overall system performance. Particle swarm optimization (PSO) algorithm is one of the most popular scheduling algorithms, which is used to maximize resource utilization. However, the performance of the PSO scheduling algorithm decreases when the number of tasks is significant. In this paper, the improved PSO (IPSO) algorithm is proposed to provide the optimal allocation for a large number of tasks. This is achieved by splitting the submitted tasks into batches in a dynamic way. The resources utilization state is considered in each creation of batches. After getting a sub-optimal solution for each batch, the algorithm appends all the sub-optimal solutions for batches into a final allocation map. Finally, IPSO tries to balance the loads over the final allocation map. The proposed algorithm is compared with different scheduling algorithms, namely, honey bee, ant colony, and round-robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of makespan, standard deviation of load, and degree of imbalance.INDEX TERMS Cloud computing, task scheduling, load balancing, particle swarm optimization.

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A study on modified PSO algorithm in cloud computing

Cited by 10 publications

References 5 publications

Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System

Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System

Multi-objective based Cloud Task Scheduling Model with Improved Particle Swarm Optimization

IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

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