Inertia Weight Controlled PSO for Task Scheduling in Cloud Computing

Kumar, Niraj; Sharma, Sanjay Kumar

doi:10.1109/gucon.2018.8674994

Cited by 10 publications

(16 citation statements)

References 10 publications

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“…As the BP neural network algorithm is based on gradient, it has the disadvantages of slow convergence and is easily trapped into the local optimal solution and sensitive to the value of weight and biases during network initialization [24]. PSO algorithm is introduced to FDA-BP model, which has better robustness and global search ability based on the swarm intelligence theory.…”

Section: Model Of Fda-bpmentioning

confidence: 99%

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Frequency Diverse Array Target Localization Based on IPSO-BP

Liu

Ding

et al. 2020

International Journal of Antennas and Propagation

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For the traditional target localization algorithms of frequency diverse array (FDA), there are some problems such as angle and distance coupling in single-frequency receiving FDA mode, large amount of calculation, and weak adaptability. This paper introduces a good learning and predictive method of target localization by using BP neural network on FDA, and FDA-IPSO-BP neural network algorithm is formed. The improved particle swarm optimization (IPSO) algorithm with nonlinear weights is developed to optimize the neural network weights and biases to prevent BP neural network from easily falling into local minimum points. In addition, the decoupling of angle and distance with single frequency increment is well solved. The simulation experiments show that the proposed algorithm has better target localization effect and convergence speed, compared with FDA-BP and FDA-MUSIC algorithms.

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Section: Model Of Fda-bpmentioning

confidence: 99%

“…e inertia weight PSO algorithm was proposed in [24], the essence of which is random search. Every particle in the swarm is International Journal of Antennas and Propagation considered to be massless and un-volumetric, and the process of optimization is carried out with the basis of particles' position and speed.…”

Section: Problem Of the Standard Pso Algorithmmentioning

confidence: 99%

Frequency Diverse Array Target Localization Based on IPSO-BP

Liu

Ding

et al. 2020

International Journal of Antennas and Propagation

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“…Particle swarm optimization (PSO) is one of the most popular population-based stochastic optimization [28]. PSO technique has received wide attention in recent years since it can converge to the optimization value quickly and has excellent robustness [29,30]. The aim of the current study is to determine the most appropriate values for the weights and bias of NN (i.e., optimized NN) based on the PSO algorithm [31].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Hasibi

Mahmoudian

Khayati

2020

IJE

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This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 ᵒC for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R 2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by R 2 = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

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“…It was compared with the other algorithms and the proposed algorithm reduces the total processing cost. Kumar, et.al, [23] proposed a new strategy for PSO algorithm by changing the inertia weights using the modified simple random inertia weight and modified oscillating inertia weight techniques. This strategy was implemented by using the cloudsim simulator.…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

“…This was simulated on the cloudsim and the proposed algorithm achieved a good results for the makespan, standard deviation and degree of imbalance when compared with the Honey bee and ant colony algorithms. PSO Algorithm Workflow Scheduling Mathematical Model --- [5] SA and PSO algorithms Task Scheduling Simulation Cloudsim [6] PSO algorithm Task Scheduling Simulation Cloudsim [7] PSO Algorithm Task Scheduling Simulation Cloudsim [8] PSO Algorithm Task Scheduling Simulation Cloudsim [9] Modified PSOAlgorithm Task Scheduling Simulation Cloudsim [10] MDAPSO algorithm Task Scheduling Simulation Cloudsim [11] LBMPSO algorithm Task Scheduling Simulation Cloudsim [12] PSO Algorithm Task Scheduing Mathematical Model ---- [13] Enhanced PSO algorithm, Task Scheduling Mathematical Model --- [14] BPSO Algorithm Workflow Scheduling Mathematical Model --- [15] GA-PSO Algorithm Workflow Scheduling Simulation Workflowsim [16] ACO and PSO Algorithms Workflow Scheduling Simulation Workflowsim [17] Modified PSO algorithm Workflow Scheduling Simulation Workflowsim [18] LDIW model Task Scheduling Simulation Cloudsim [19] PSO and GA Algorithms Task Scheduling Simulation Cloudsim [20] Symbiotic Organism Search Task Scheduling Simulation Cloudsim [21] Extended PSO Task Scheduling Simulation Cloudsim [22] modified PSO Task Scheduling Simulation Cloudsim [23] PSO Algorithm Task Scheduling Simulation Cloudsim [24] modified PSO Task Scheduling Real time implementation Amazon EC2 and Google cloud [25] GA and PSO Task Scheduling Mathematical Model --- [26] PSO and Hill Climbing Algorithms Task Scheduling Mathematical Model --- [27] Modified PSO Task Scheduling Mathematical Model --- [28] Modified PSO Task Scheduling Simulation Cloudsim [29] PSO Algorithm Task Scheduling Simulation Cloudsim [30] IPSO Algorithm Task Scheduling Simulation Cloudsim…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019

IJRTE

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Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

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Inertia Weight Controlled PSO for Task Scheduling in Cloud Computing

Cited by 10 publications

References 10 publications

Frequency Diverse Array Target Localization Based on IPSO-BP

Frequency Diverse Array Target Localization Based on IPSO-BP

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

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