Eigenfrequency-based topology optimization using cooperative coevolutionary strategies and moving morphable components

This paper aims to present and investigate a derivative-free design optimization methodology for continuum structures to maximize the eigenfrequencies with different constraints. The recently developed moving morphable component method is used as the parameterization technique which is so suitable f...

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Bibliographic Details
Published in:Journal of the Brazilian Society of Mechanical Sciences and Engineering 2022-05, Vol.44 (5), Article 194
Main Authors: Rostami, Pooya, Marzbanrad, Javad, Taghavi Parsa, Mohammad Hossein
Format: Article
Language:English
Subjects:
Algorithms
Design optimization
Engineering
Mathematical analysis
Maximization
Mechanical Engineering
Parameterization
Resonant frequencies
Sensitivity analysis
Strategy
Technical Paper
Topology optimization
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Summary:This paper aims to present and investigate a derivative-free design optimization methodology for continuum structures to maximize the eigenfrequencies with different constraints. The recently developed moving morphable component method is used as the parameterization technique which is so suitable for non-gradient optimization algorithms due to the lower numbers of design variables. Two objective functions are considered in this paper. In the first point of view, the aim is to maximize multiple eigenfrequencies. For the second strategy, the maximization of multiple frequency gaps is considered. Multiple constraints are considered in the problem definition. Instead of virtual mass, static stress and compliance are defined. Previous papers presented optimal designs only for eigenfrequency maximization and used a virtual mass to prevent discontinuity in the design domain. Herein, the topological design is performed for both static (stress and compliance) and eigenfrequency targets. Based on the results, the cooperative coevolutionary strategy coupled with MMC has a very good potential in solving eigenfrequency-related problems while needing no sensitivity analysis. Since this algorithm is so easy to implement and very successful in benchmark problems, industries can use it even for more complex cases. Also, it is observed that the algorithm can produce diverse and competitive outputs due to non-deterministic behavior. So it can be categorized as generative design tools.
ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-022-03496-w