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Neural Computing and Applications
Published in: Neural Computing and Applications 5/2013

01-10-2013 | Original Article

A multi-stage particle swarm for optimum design of truss structures

Authors: S. Talatahari, M. Kheirollahi, C. Farahmandpour, A. H. Gandomi

Published in: Neural Computing and Applications | Issue 5/2013

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Abstract

The contribution of this study is to propose a multi-stage particle swarm optimization (MSPSO) for structural optimization. In this paper, three auxiliary improving mechanisms are added to the standard particle swarm optimization (PSO) in order to enhance its efficiency and reliability dealing with optimum design of truss structures. These mechanisms effectively accelerate the convergence rate of the PSO and also make it robust to attain better optimum solutions during various runs of the algorithm. The effectiveness of the MSPSO is illustrated by several benchmark structural optimization problems. Results demonstrate the efficiency and robustness of the proposed MSPSO algorithm compared to the standard version of the PSO.
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Metadata
Title
A multi-stage particle swarm for optimum design of truss structures
Authors
S. Talatahari
M. Kheirollahi
C. Farahmandpour
A. H. Gandomi
Publication date
01-10-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1072-5

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