An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints

Structural optimization with frequency constraints is well known as a highly nonlinear and complex optimization problem with many local optimum solutions. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the comp...

Full description

Saved in:
Bibliographic Details
Published inNeural computing & applications Vol. 29; no. 1; pp. 167 - 185
Main Authors Ho-Huu, V., Nguyen-Thoi, T., Truong-Khac, T., Le-Anh, L., Vo-Duy, T.
Format Journal Article
LanguageEnglish
Published London Springer London 01.01.2018
Springer Nature B.V
Subjects
Artificial Intelligence
Basic converters
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Evolution
Image Processing and Computer Vision
Optimization
Original Article
Probability and Statistics in Computer Science
Structural optimization with frequency constraints
Differential evolution
Roulette wheel selection
Elitist selection technique
Optimization of truss structures
Online AccessGet full text
ISSN0941-0643
1433-3058
DOI10.1007/s00521-016-2426-1

Cover

More Information
Summary:Structural optimization with frequency constraints is well known as a highly nonlinear and complex optimization problem with many local optimum solutions. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the computational cost and the quality of solutions. In this work, a novel differential evolution (DE) is proposed to solve the shape and size optimization problems for truss structures with frequency constraints. The proposed method, called ReDE, is a new version of the DE algorithm with two improvements. Firstly, the roulette wheel selection is employed to choose members for the mutation phase instead of random selection as in the conventional DE. Secondly, an elitist selection technique is applied to the selection phase instead of basic selection to improve the convergence speed of the method. The efficiency and reliability of the proposed method are demonstrated through five numerical examples. Numerical results reveal that the proposed algorithm outperforms many optimization methods in the literature.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2426-1