Comparison of Deterministic and Stochastic Optimization Algorithms for Generic Wing Design Problems

Wang, Xuehang; Damodaran, M.

doi:10.2514/2.2695

Cited by 17 publications

(2 citation statements)

References 3 publications

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“…There are many problems in this field-including structure weight optimization, stress reduction, elastic and aeroelastic characterizations, and thermal resilience-which need appropriate optimization methods to be solved. In complex computational-method-based structural problems where deterministic algorithms are not applicable, the stochastic nature-inspired algorithm can provide acceptable and relatively fast results [443]. These algorithms can also be used for optimal finite element model updates in structural analysis.…”

Section: Structurementioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

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Section: Structurementioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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show abstract

“…The division algorithm of Aarts and Korst 9 and the three parallelization strategies of Diekmann et al 10 are also widely used. Wang and Damodaran 11,12 used parallel simulated annealing (PSA) to reduce the number of evaluations of the objective function for each processor and wall-clock time for a number of representative aerodynamic shape design optimization problems. Applications of parallel GA to improve the computational efficiency of aerodynamic design problems have also been reported by Vicini and Quagliarella 13 and Hämäläinen et al 14 Although parallel SA and GA can speed up the computation, they still require enormous computational resources and effort.…”

Section: Introductionmentioning

confidence: 99%

Application of Simultaneous Perturbation Stochastic Approximation Method for Aerodynamic Shape Design Optimization

Xing

Damodaran

2005

AIAA Journal

Self Cite

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Aerodynamic shape design optimization problems, such as inverse and constrained airfoil design and axisymmetric nozzle design, are investigated by applying the simultaneous perturbation stochastic approximation (SPSA) method to objective functions that are estimated during each design iteration using a finite volume computational fluid dynamics technique for solving the compressible Navier-Stokes equations. The SPSA method has been demonstrated in the literature as having significant advantages over stochastic global optimization methods such as the simulated annealing (SA) method. In this work the SPSA is compared with SA method for a class of twodimensional and axisymmetric aerodynamic design optimization problems. The numerical studies show that the SPSA method is robust in reaching optimal aerodynamic shapes, is easy to implement, and is highly efficient. The SPSA method can also decrease the computational costs significantly compared with the SA method.

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General Aspects of the Application of Computational Methods in Industry 4.0

Karkalos

Markopoulos

Davim

2018

Computational Methods for Application in Industry 4.0

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Comparison of Deterministic and Stochastic Optimization Algorithms for Generic Wing Design Problems

Cited by 17 publications

References 3 publications

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Application of Simultaneous Perturbation Stochastic Approximation Method for Aerodynamic Shape Design Optimization

General Aspects of the Application of Computational Methods in Industry 4.0

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