A Novel Global Optimization Algorithm and Its Application to Airfoil Optimization

In this paper, a novel global optimization algorithm has been developed, which is named as particle swarm optimization combined with particle generator (PSO–PG). In PSO–PG, a PG was introduced to iteratively generate the initial particles for PSO. Based on a series of comparable numerical experiments, it was convinced that the calculation accuracy of the new algorithm as well as its optimization efficiency was greatly improved in comparison with those of the standard PSO. It was also observed that the optimization results obtained from PSO–PG were almost independent of some critical coefficients employed in the algorithm. Additionally, the novel optimization algorithm was adopted in the airfoil optimization. A special fitness function was designed and its elements were carefully selected for the low-velocity airfoil. To testify the accuracy of the optimization method, the comparative experiments were also carried out to illustrate the difference of the aerodynamic performance between the optimized and its initial airfoil.

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A Novel Global Optimization Algorithm and its Application to Airfoil Optimization

Volume 1A: Aircraft Engine; Fans and Blowers ◽

10.1115/gt2014-25013 ◽

2014 ◽

Author(s):

B. Yang ◽

Q. Xu ◽

L. He ◽

L. H. Zhao ◽

Ch. G. Gu ◽

...

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Fitness Function ◽

Optimization Method ◽

Global Optimization Algorithm ◽

Low Velocity ◽

Airfoil Optimization ◽

The Difference ◽

Optimization Efficiency ◽

Particle Generator

In this paper, a novel global optimization algorithm has been developed, which is named as Particle Swarm Optimization combined with Particle Generator (PSO-PG). In PSO-PG, a particle generator was introduced to iteratively generate the initial particles for PSO. Based on a series of comparable numerical experiments, it was convinced that the calculation accuracy of the new algorithm as well as its optimization efficiency was greatly improved in comparison with those of the standard PSO. It was also observed that the optimization results obtained from PSO-PG were almost independent of some critical coefficients employed in the algorithm. Additionally, the novel optimization algorithm was adopted in the airfoil optimization. A special fitness function was designed and its elements were carefully selected for the low-velocity airfoil. To testify the accuracy of the optimization method, the comparative experiments were also carried out to illustrate the difference of the aerodynamic performance between the optimized and its initial airfoil.

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A multiple-data-based efficient global optimization algorithm and its parallel implementation for automotive body design

Advances in Mechanical Engineering ◽

10.1177/1687814018794341 ◽

2018 ◽

Vol 10 (8) ◽

pp. 168781401879434 ◽

Cited By ~ 1

Author(s):

Bing Xu ◽

Yong Cai

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Graphics Processing Unit ◽

Optimization Method ◽

Efficient Global Optimization ◽

Processing Unit ◽

Global Optimization Algorithm ◽

Multiple Data ◽

Practical Engineering ◽

Graphics Processing

The purpose of this article is to improve the convergence efficiency of the traditional efficient global optimization method. Furthermore, we try a graphics processing unit–based parallel computing method to improve the computing efficiency of the efficient global optimization method for both mathematical and practical engineering problems. First, we propose a multiple-data-based efficient global optimization algorithm instead of the multiple-surrogates-based efficient global optimization algorithm. Second, a novel graphics processing unit–based general-purpose computing technology is adopted to accelerate the solution efficiency of our multiple-data-based efficient global optimization algorithm. Third, a hybrid parallel computing approach using the OpenMP and compute unified device architecture is adopted to further improve the solution efficiency of forward problems in practical application. This is accomplished by integrating the graphics processing unit–based finite element method numerical analysis system into the optimization software. The numerical results show that for the same problem, the optimal result of the multiple-data-based efficient global optimization algorithm is consistently better than the multiple-surrogates-based efficient global optimization algorithm with the same optimization iterations. In addition, the graphics processing unit–based parallel simulation system helps in the reduction of the calculation time for practical engineering problems. The multiple-data-based efficient global optimization method performs stably in both high-order mathematical functions and large-scale nonlinear practical engineering optimization problems. An added benefit is that the computational time and accuracy are no longer obstacles.

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