Generalized Random Tunneling Algorithm for Continuous Design Variables

2003 ◽  
Author(s):  
Satoshi Kitayama ◽  
Koetsu Yamazaki
Keyword(s):  
Global Optimization ◽  
Mathematical Programming ◽  
Structural Optimization ◽  
Global Minimum ◽  
Optimization Problems ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Numerical Examples ◽  
Design Variables ◽  
Side Constraints

A global optimization method for continuous design variables called as Generalized Random Tunneling Algorithm is proposed. Proposed method is called as “Generalized” random tunneling algorithm because this method can treat the behavior constraints as well as the side constraints. This method consists of three phases, that is, the minimization phase, the tunneling phase, and the constraints phase. In the minimization phase, mathematical programming is used, and heuristic approach is introduced in the tunneling and constraint phases. By iterating these phases, global minimum is found. The characteristics of mathematical programming and heuristic approaches are included in the proposed method. Global minimum which may lie on the boundary of constraints is easily found by proposed method. Proposed method is applied to mathematical and structural optimization problems. Through numerical examples, the effectiveness and validity of proposed method have been confirmed.

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.

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.

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.

Two-Level Structural Optimization Method for Multi-Processor Environment

1990 ◽  
Author(s):  
Ching-Kuo Hsiung ◽  
Mohamed E. M. El-Sayed
Keyword(s):  
Structural Optimization ◽  
Lower Bounds ◽  
Optimization Problem ◽  
Optimization Method ◽  
Global Constraints ◽  
Optimization Approach ◽  
Final Solution ◽  
Local Constraints ◽  
Numerical Examples ◽  
Design Variables

Abstract In this paper a two-level structural optimization approach is presented. At the first level, the objective of the optimization problem is to minimize the total weight of the whole structure subject to the global constraints. At the second level, the optimization problem is divided into several subproblems, each subproblem represents a substructure. The objective of each subproblem is to minimize the weight of the assigned substructure subject to its local constraints. To assure that the final solution will not violate the global constraints, the optimum values of the design variables from the first level are used to update the lower bounds on these variables at the second level. Two numerical examples are included to demonstrate the approach and its application in a multi-processor environment.

Product Optimization Incorporating Discrete Design Variables Based on Decomposition of Performance Characteristics

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Masataka Yoshimura ◽  
Yu Yoshimura ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Mathematical Programming ◽  
Optimization Problems ◽  
System Optimization ◽  
Optimization Method ◽  
Hierarchical Optimization ◽  
Standard Material ◽  
Design Variables ◽  
Hierarchical Levels ◽  
Discrete Design Variables ◽  
Product Optimization

This paper proposes a system optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics from original characteristics. The method is constructed to take advantage of hierarchical optimization procedures, enabling the incorporation of discrete design variables. The proposed method can be applied to machine product designs that include discrete design variables such as material types, machining methods, standard material forms, and specifications. The optimizations begin at the lowest levels of the hierarchical optimization structure and proceed to the higher levels. Discrete design variables are efficiently selected and optimized in the form of small suboptimization problems at the lowest hierarchical levels, and optimum solutions for the entire problem are ultimately obtained using conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems that include several types of discrete design variables are constructed, and applied examples are provided to demonstrate their effectiveness.

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 Dynamic Optimization Design Method for the Turbine Engine Frame Foundation

2012 ◽  
Vol 226-228 ◽  
pp. 784-787
Author(s):  
Zhao Jun Li ◽  
Xi Cheng Wang
Keyword(s):  
Dynamic Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Design Method ◽  
Kriging Model ◽  
Optimization Method ◽  
Turbine Engine ◽  
Design Variables ◽  
Side Constraints ◽  
Dynamic Reanalysis

An effective optimization method using Kriging model and parametric sampling evaluation strategy is proposed to solve dynamic optimization design. The optimization problem is to find the design variables such that the structural weight is minimum and dynamic displacement of the points concerned plus certain side constraints are satisfied. The types of design variables are considered as the sizing variables of the beams and columns. Kriging model is used to build the approximate mapping relationship between the forced vibration amplitude and design variables, reducing expensive dynamic reanalysis. A dynamic analysis program is used as black-box to obtain dynamic response. Numerical examples show that the method has good accuracy and efficiency. Versatility of this method can be expected to play an important role in future engineering optimization problems.

Global Optimization Method Based on Incremental Radial Basis Functions

2011 ◽  
Vol 121-126 ◽  
pp. 3950-3954
Author(s):  
Xin Wei ◽  
Yi Zhong Wu ◽  
Li Ping Chen
Keyword(s):  
Global Optimization ◽  
Global Minimum ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Global Optimization Method ◽  
Latin Hypercube Design ◽  
Sampling Step ◽  
Method Base ◽  
Engineering Problems ◽  
Complex Engineering

Global optimization techniques have been used extensively due to their capability in handling complex engineering problems. Metamodel becomes effective method to enhance global optimization. In this paper, we propose a new global optimization method base on incremental metamodel. At each sampling step, we adopt inherited Latin HyperCube design to sample points step by step, and propose a new incremental metamodel to update the cofficient matrix gradually. Experiments proved that the global optimization method has highest efficiency and can be finding global minimum fastly.

Sign in / Sign up

Export Citation Format

Share Document