Multi-Objective Parametric Optimization Design for Mirrors Combined with Non-Dominated Sorting Genetic Algorithm

The process of intelligent multi-objective parametric optimization design for mirrors is discussed in detail in this paper, with the error of the mirror surface shape and the total mass being examined as the optimization objectives. The establishment of complex objective functions for solving the optimization problem of the mirror surface shape error was realized, and manual modification of the model was avoided. Moreover, combining this with a non-dominated sorting genetic algorithm (NSGA) helped the Pareto front move towards an ideal optimal set of solutions. To verify the effectiveness of the proposed method, an aluminum alloy mirror with an aperture of 140 mm was taken as an example. The Pareto optimal solution set of the mass and surface shape error under 1 g gravity was obtained for finding the required solution and satisfying the optimization goal. In addition, this method is applicable to other complex structural design problems..

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:13

Enthalten in:

Applied Sciences - 13(2023), 5, p 3346

Sprache:

Englisch

Beteiligte Personen:

Lu Sun [VerfasserIn]
Bao Zhang [VerfasserIn]
Ping Wang [VerfasserIn]
Zhihong Gan [VerfasserIn]
Pengpeng Han [VerfasserIn]
Yijian Wang [VerfasserIn]

Links:

doi.org [kostenfrei]
doaj.org [kostenfrei]
www.mdpi.com [kostenfrei]
Journal toc [kostenfrei]

Themen:

Biology (General)
Chemistry
Engineering (General). Civil engineering (General)
Intelligent parameter optimization design
Joint simulation
Mirror surface shape error
Non-dominated sorting genetic algorithm
Pareto optimal solution set
Physics
T
Technology

doi:

10.3390/app13053346

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

DOAJ088033503