Group Search Optimization for Applications in Structural Design

Group Search Optimization for Applications in Structural Design
Author :
Publisher : Springer Science & Business Media
Total Pages : 257
Release :
ISBN-10 : 9783642205361
ISBN-13 : 3642205364
Rating : 4/5 (364 Downloads)

Book Synopsis Group Search Optimization for Applications in Structural Design by : Lijuan Li

Download or read book Group Search Optimization for Applications in Structural Design written by Lijuan Li and published by Springer Science & Business Media. This book was released on 2011-05-27 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Civil engineering structures such as buildings, bridges, stadiums, and offshore structures play an import role in our daily life. However, constructing these structures requires lots of budget. Thus, how to cost-efficiently design structures satisfying all required design constraints is an important factor to structural engineers. Traditionally, mathematical gradient-based optimal techniques have been applied to the design of optimal structures. While, many practical engineering optimal problems are very complex and hard to solve by traditional method. In the past few decades, swarm intelligence algorithms, which were inspired by the social behaviour of natural animals such as fish schooling and bird flocking, were developed because they do not require conventional mathematical assumptions and thus possess better global search abilities than the traditional optimization algorithms and have attracted more and more attention. These intelligent based algorithms are very suitable for continuous and discrete design variable problems such as ready-made structural members and have been vigorously applied to various structural design problems and obtained good results. This book gathers the authors’ latest research work related with particle swarm optimizer algorithm and group search optimizer algorithm as well as their application to structural optimal design. The readers can understand the full spectrum of the algorithms and apply the algorithms to their own research problems.


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