An evolutionary optimization-based approach for simulation of endurance time load functions
Affiliation
Univ Arizona, Dept Civil Engn & Engn MechIssue Date
2019-02-01Keywords
Endurance time methodimperialist competitive algorithm
dynamic analysis
discrete wavelet transform
classical optimization methods
simulated annealing
Metadata
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TAYLOR & FRANCIS LTDCitation
Mohammadreza Mashayekhi, Homayoon E. Estekanchi, Hassan Vafai & Goodarz Ahmadi (2019) An evolutionary optimization-based approach for simulation of endurance time load functions, Engineering Optimization, 51:12, 2069-2088, DOI: 10.1080/0305215X.2019.1567724Journal
ENGINEERING OPTIMIZATIONRights
Copyright © 2019 Informa UK Limited, trading as Taylor & Francis Group.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
A novel optimization method based on Imperialist Competitive Algorithm (ICA) for simulating endurance time (ET) excitations was proposed. The ET excitations are monotonically intensifying acceleration time histories that are used as dynamic loading. Simulation of ET excitations by using evolutionary algorithms has been challenging due to the presence of a large number of decision variables that are highly correlated due to the dynamic nature of the problem. Optimal parameter values of the ICA algorithm for simulating ETEFs were evaluated and were used to simulate ET excitations. In order to increase the capability of the ICA and provide further search in the optimization space, this algorithm was combined with simulated annealing (SA). The new excitation results were compared with the current practice for simulation of ET excitations. It was shown that the proposed ICA-SA method leads to more accurate ET excitations than the classical optimization methods.Note
12 month embargo; published online: 1 February 2019ISSN
0305-215XVersion
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1080/0305215x.2019.1567724