Group Search Optimization for Applications in Structural Design

Group Search Optimization for Applications in Structural Design

AngličtinaPevná vazba
Li Lijuan
Springer, Berlin
EAN: 9783642205354
Na objednávku
Předpokládané dodání v pondělí, 27. ledna 2025
3 949 Kč
Běžná cena: 4 388 Kč
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Podrobné informace

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 basedalgorithms 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.

 

EAN 9783642205354
ISBN 3642205356
Typ produktu Pevná vazba
Vydavatel Springer, Berlin
Datum vydání 27. května 2011
Stránky 250
Jazyk English
Rozměry 235 x 155
Země Germany
Sekce Professional & Scholarly
Autoři Li Lijuan; LIU FENG
Ilustrace X, 250 p.
Edice 2011 ed.
Série Adaptation, Learning, and Optimization