Identification of a Box-Jenkins structured two stage cascaded model using Simplex Particle Swarm Optimization algorithm

2016 ◽  
Author(s):  
P. S. Pal ◽  
A. Dasgupta ◽  
J. R. Akhil ◽  
R. Kar ◽  
D. Mandal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Two Stage ◽  
Swarm Optimization ◽  
Cascaded Model

scholarly journals A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

2017 ◽  
Vol 14 (8) ◽  
pp. 694-702 ◽  
Author(s):  
Mingxuan Mao ◽  
Li Zhang ◽  
Qichang Duan ◽  
O.J.K Oghorada ◽  
Pan Duan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Two Stage ◽  
Swarm Optimization

scholarly journals A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 488
Author(s):  
Yinghui Meng ◽  
Qianying Zhi ◽  
Qiuwen Zhang ◽  
Ni Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Two Stage ◽  
Wireless Sensor Nodes ◽  
Swarm Optimization

At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in “concave regions”. In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with “concave regions”, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes.

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