Replication of results

Haftka, Raphael T.; Zhou, Ming; Queipo, Néstor V.

doi:10.1007/s00158-019-02298-4

Cited by 8 publications

(1 citation statement)

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“…The code has no public access, as well as shown models. Following suggestions from Haftka et al (2019), to improve the replication of results, the proposed method is implemented in the python script (see additional materials). The script has all significant steps of the relaxed gradient projection method, and it solves a constrained quadratic problem.…”

Section: Compliance With Ethical Standardsmentioning

confidence: 99%

Relaxed gradient projection algorithm for constrained node-based shape optimization

Antonau¹,

Hojjat

Bletzinger³

2021

Struct Multidisc Optim

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In node-based shape optimization, there are a vast amount of design parameters, and the objectives, as well as the physical constraints, are non-linear in state and design. Robust optimization algorithms are required. The methods of feasible directions are widely used in practical optimization problems and know to be quite robust. A subclass of these methods is the gradient projection method. It is an active-set method, it can be used with equality and non-equality constraints, and it has gained significant popularity for its intuitive implementation. One significant issue around efficiency is that the algorithm may suffer from zigzagging behavior while it follows non-linear design boundaries. In this work, we propose a modification to Rosen’s gradient projection algorithm. It includes the efficient techniques to damp the zigzagging behavior of the original algorithm while following the non-linear design boundaries, thus improving the performance of the method.

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Section: Compliance With Ethical Standardsmentioning

confidence: 99%