Abstract

This paper is the first of two-part series on a uniformly sampled genetic algorithm with gradient search devised to efficiently solve the optimization-based structural identification. The strategy involves multi-species exploration, adaptive search space reduction and quasi-random sequence sampling. The use of a small number of uniform samples enables preliminary exploration in the solution space so as to shorten the “learning curve” considerably. The proposed strategy is shown by numerical study to give much better identification accuracy than the original search space reduction method, while using much less computational time for identification of known-mass and unknown-mass systems.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.