scholarly journals Design of dimensionally stable composites using efficient global optimization method

2016 ◽  
Vol 233 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Levent Aydin ◽  
Olgun Aydin ◽  
H Seçil Artem ◽  
Ali Mert
Keyword(s):  
Global Optimization ◽  
Communication Systems ◽  
Optimization Problems ◽  
Material Design ◽  
Optimization Method ◽  
Pattern Search ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Moisture Expansion ◽  
Design And Optimization

Dimensionally stable material design is an important issue for space structures such as space laser communication systems, telescopes, and satellites. Suitably designed composite materials for this purpose can meet the functional and structural requirements. In this paper, it is aimed to design the dimensionally stable laminated composites by using efficient global optimization method. For this purpose, the composite plate optimization problems have been solved for high stiffness and low coefficients of thermal and moisture expansion. Some of the results based on efficient global optimization solution have been verified by genetic algorithm, simulated annealing, and generalized pattern search solutions from the previous studies. The proposed optimization algorithm is also validated experimentally. After completing the design and optimization process, failure analysis of the optimized composites has been performed based on Tsai–Hill, Tsai–Wu, Hoffman, and Hashin–Rotem criteria.

A New Global Optimization Method for Simultaneous Computation on Expensive Black-Box Functions

2003 ◽  
Author(s):  
Liqun Wang ◽  
Songqing Shan ◽  
G. Gary Wang
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Black Box ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Global Optimization Methods

The presence of black-box functions in engineering design, which are usually computation-intensive, demands efficient global optimization methods. This work proposes a new global optimization method for black-box functions. The global optimization method is based on a novel mode-pursuing sampling (MPS) method which systematically generates more sample points in the neighborhood of the function mode while statistically covers the entire search space. Quadratic regression is performed to detect the region containing the global optimum. The sampling and detection process iterates until the global optimum is obtained. Through intensive testing, this method is found to be effective, efficient, robust, and applicable to both continuous and discontinuous functions. It supports simultaneous computation and applies to both unconstrained and constrained optimization problems. Because it does not call any existing global optimization tool, it can be used as a standalone global optimization method for inexpensive problems as well. Limitation of the method is also identified and discussed.

scholarly journals A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box

2016 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Global Optimization Method

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

A Novel Global Optimization Method – Genetic Pattern Search

2010 ◽  
Vol 44-47 ◽  
pp. 3240-3244 ◽  
Author(s):  
Yu Dong Zhang ◽  
Le Nan Wu ◽  
Yuan Kai Huo ◽  
Shui Hua Wang
Keyword(s):  
Global Optimization ◽  
Computation Time ◽  
Optimization Method ◽  
Pattern Search ◽  
Test Functions ◽  
Global Optimization Method ◽  
Minimal Points ◽  
Genetic Pattern ◽  
Two Phases ◽  
Coarse Search

A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.

scholarly journals An analytical framework for consensus-based global optimization method

2018 ◽  
Vol 28 (06) ◽  
pp. 1037-1066 ◽  
Author(s):  
José A. Carrillo ◽  
Young-Pil Choi ◽  
Claudia Totzeck ◽  
Oliver Tse
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Mean Field ◽  
Optimization Method ◽  
Analytical Framework ◽  
Global Minimizer ◽  
Global Optimization Method ◽  
The Mean ◽  
Class Of Functions ◽  
Theoretical Results

In this paper, we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions. Theoretical results on consensus estimates are then illustrated by numerical simulations where variants of the method including nonlinear diffusion are introduced.

scholarly journals Genetic Pattern Search and Its Application to Brain Image Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yudong Zhang ◽  
Shuihua Wang ◽  
Genlin Ji ◽  
Zhengchao Dong
Keyword(s):  
Global Optimization ◽  
Brain Mri ◽  
Global Solutions ◽  
Optimization Method ◽  
Pattern Search ◽  
The Novel ◽  
Global Optimization Method ◽  
Limited Region ◽  
Minimal Points ◽  
Genetic Pattern

A novel global optimization method, based on the combination of genetic algorithm (GA) and generalized pattern search (PS) algorithm, is proposed to find global minimal points more effectively and rapidly. The idea lies in the facts that GA tends to be quite good at finding generally good global solutions, but quite inefficient in finding the last few mutations for the absolute optimum, and that PS is quite efficient in finding absolute optimum in a limited region. The novel algorithm, named as genetic pattern search (GPS), employs the GA as the search method at every step of PS. Experiments on five different classical benchmark functions (consisting of Hump, Powell, Rosenbrock, Schaffer, and Woods) demonstrate that the proposed GPS is superior to improved GA and improved PS with respect to success rate. We applied the GPS to the classification of normal and abnormal structural brain MRI images. The results indicate that GPS exceeds BP, MBP, IGA, and IPS in terms of classification accuracy. This suggests that GPS is an effective and viable global optimization method and can be applied to brain MRI classification.

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