A Robust Global Optimization Algorithm of Electromagnetic Devices Utilizing Gradient Index and Multi-Objective Optimization Method

2011 ◽  
Vol 47 (5) ◽  
pp. 1254-1257 ◽  
Author(s):  
Ziyan Ren ◽  
Minh-Trien Pham ◽  
Minho Song ◽  
Dong-Hun Kim ◽  
Chang Seop Koh
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Gradient Index ◽  
Multi Objective Optimization ◽  
Global Optimization Algorithm ◽  
Multi Objective ◽  
Electromagnetic Devices

A Novel Global Optimization Algorithm and Its Application to Airfoil Optimization

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
B. Yang ◽  
Q. Xu ◽  
L. He ◽  
L. H. Zhao ◽  
Ch. G. Gu ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Optimization Method ◽  
The Novel ◽  
Global Optimization Algorithm ◽  
Low Velocity ◽  
Airfoil Optimization ◽  
The Difference ◽  
Optimization Efficiency

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.

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

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879434 ◽  
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.

On the arithmetic of infinity oriented implementation of the multi-objective P-algorithm

2012 ◽  
Author(s):  
Antanas Žilinskas
Keyword(s):  
Global Optimization ◽  
Black Box ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Simple Application ◽  
Axiomatic Theory ◽  
Global Optimization Algorithm ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Single Objective

The single-objective P-algorithm is a global optimization algorithm based on a statistical mod- el of objective functions and the axiomatic theory of rational decisions. It has been proven quite suitable for optimization of black-box expensive functions. Recently the P-algorithm has been generalized to multi-objective optimization. In the present paper, the implementation of that algorithm is considered using the new computing paradigm of the arithmetic of infinity. A strong homogeneity of the multi-objective P-algorithm is proven, thus enabling rather a simple application of the algorithm to the problems involving infinities and infinitesimals.

A Novel Global Optimization Algorithm and its Application to Airfoil Optimization

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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