Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

2011 ◽  
Vol 38 (6) ◽  
pp. 6405-6411 ◽  
Author(s):  
Qi Wu ◽  
Rob Law
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Parameters Selection ◽  
Cauchy Mutation ◽  
Objective Variable ◽  
Selection Of

Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy

2014 ◽  
Vol 1049-1050 ◽  
pp. 1654-1657
Author(s):  
Jie Liu ◽  
Xu Sheng Gan ◽  
Wen Ming Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Parameters Selection ◽  
Sharing Strategy ◽  
Selection Of

To optimize the parameters of LS-SVM effectively, an improved Particle Swarm Optimization (PSO) algorithm is proposed to select the optimal parameters combination. For the improvement of the precocity in PSO algorithm, an multi-particles sharing strategy is introduced in simple PSO algorithm to enhance the convergence. The simulation indicates that the proposed PSO algorithm has a better selection on LS-SVM parameters.

scholarly journals Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

2020 ◽  
Vol 13 (6) ◽  
pp. 42-51
Author(s):  
Archana Kollu ◽  
◽  
Sucharita Vadlamudi ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Energy Management ◽  
Optimization Technique ◽  
Service Level Agreement ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cauchy Mutation ◽  
Eagle Strategy

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

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