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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

02-03-2018 | Original Article

Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator

Authors: Hirad Assimi, Ali Jamali, Nader Nariman-zadeh

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Most real-world engineering problems deal with multiple conflicting objectives simultaneously. In order to address this issue in truss optimization, this paper presents a multi-objective genetic programming approach for sizing and topology optimization of trusses. It aims to find the optimal cross-sectional areas and connectivities between the nodes to achieve a set of trade-off solutions to satisfy all the optimization objective functions subjected to some constraints such as kinematic stability, maximum allowable stress in members and nodal deflections. It also uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the potential final set of solutions. This approach also employs an adaptive mutant factor besides the classical genetic operators to improve the exploring capabilities of Genetic Programming in structural optimization. The intrinsic features of genetic programming help to identify redundant truss members and nodes in the design space, while no violation of constraints occurs. Our approach applied to some numerical examples and found a better non-dominated solution set in the most cases in comparison with the competent methods available in the literature.
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Metadata
Title
Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator
Authors
Hirad Assimi
Ali Jamali
Nader Nariman-zadeh
Publication date
02-03-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3401-9

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