DocumentCode
3273716
Title
The Back Analysis of Mechanics Parameters Based on DEPSO Algorithm and Parallel FEM
Author
Huang, He ; Wei, Zhihua ; Li, Zhuoqiu ; Rao, Wenbi
Author_Institution
Coll. of Sci., Wuhan Univ. of Technol., Wuhan, China
Volume
1
fYear
2009
fDate
6-7 June 2009
Firstpage
81
Lastpage
84
Abstract
The back-analysis of mechanics parameters needs iterative forward calculating, resulting in low efficiency; meanwhile the Particle Swarm Optimization (PSO) algorithm and other optimization algorithms are exposed to local optimum possibilities. In this paper, DEPSO-ParallelFEM, a system integrated of an algorithm of hybrid particle swarm with Differential Evolution (DE) operator, termed DEPSO, and parallel Finite Element Method (FEM), is proposed to solve these problems. DEPSO guarantees the particle to escape from local minima by enhancing particlepsilas diversity through the combination of PSO operator and DE operator; and parallel FEM is applied to improve the computing speed and precision by adopting the techniques of Cluster of Workstation (COW), MPI, Domain Decomposition Method (DDM), and Object-Oriented Programming (OOP) and so on. A computational example proves that this system is of excellent parameter exploration capability and high speed; thus it is of great academic value and significant applicable value.
Keywords
finite element analysis; particle swarm optimisation; DEPSO algorithm; MPI; differential evolution operator; domain decomposition method; mechanic parameters back analysis; object-oriented programming; parallel FEM; parallel finite element method; particle diversity; particle swarm optimization algorithm; workstation cluster; Algorithm design and analysis; Concurrent computing; Distributed decision making; Diversity reception; Educational institutions; Evolutionary computation; Finite element methods; Iterative algorithms; Particle swarm optimization; Workstations; COW; DEPSO; FEM; back-analysis; parallel;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3645-3
Type
conf
DOI
10.1109/CINC.2009.129
Filename
5231470
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