scholarly journals Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

2019 ◽  
Vol 13 (5) ◽  
pp. 1-27 ◽  
Author(s):  
Yu Xue ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Scale Feature ◽  
Self Adaptive

Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

2012 ◽  
pp. 88-107 ◽  
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
R. Vasanth Ram
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Resource Dynamics ◽  
Virtual Machine Provisioning ◽  
Dynamic Resource ◽  
Self Adaptive

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.

Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

2011 ◽  
Vol 7 (2) ◽  
pp. 25-44 ◽  
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
R. Vasanth Ram
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Resource Dynamics ◽  
Virtual Machine Provisioning ◽  
Dynamic Resource ◽  
Self Adaptive

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.

Fault Diagnosis of Grounding Grid Based on Self-Adaptive Particle Swarm Optimization

2013 ◽  
Vol 448-453 ◽  
pp. 1937-1940
Author(s):  
Wen Hao Lan ◽  
Yi Hui Zheng ◽  
Li Xue Li ◽  
Xin Wang ◽  
Gang Yao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Minimum Energy ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
New Method ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Grounding Grid ◽  
Self Adaptive

To make effective fault diagnosis of grounding grid , a new method using Self-Adaptive Particle Swarm Optimization (SAPSO) is proposed. Firstly, the grounding grid can be handled as a resistive network to establish fault diagnosis equations. Then the objective function based on minimum energy principle is added to lower the ill-condition of diagnostic equation. Next, according to optimization techniques, a new method of SAPSO is proposed to solve the corrosion diagnosis equations. The method takes advantage of the high global searching ability of SAPSO to obtain the optimal solution to the diagnosis model. By means of the analysis of the simulation, the correctness and reliability of the method have been verified.

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