Interactive Computing in the Application of Monotonicity Analysis to Design Optimization

1983 ◽  
Vol 105 (2) ◽  
pp. 181-186 ◽  
Author(s):  
J. Zhou ◽  
R. W. Mayne
Keyword(s):  
Computer Program ◽  
Design Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Active Part ◽  
Computer Algorithm ◽  
Interactive Computing ◽  
Number Of Publications ◽  
Monotonicity Analysis

The use of monotonicity analysis in design optimization has been demonstrated in a number of publications in recent years. The purpose of this paper is to indicate the possibility of implementing the concepts of monotonicity analysis in a computer algorithm. The computer program makes the monotonicity decisions. The user is asked to adjust the optimization problem accordingly and takes an active part in the solution process.

A Multi-Level Optimization-Based Design Procedure Using Global Monotonicity Analysis

1988 ◽  
Author(s):  
S. Azarm ◽  
W.-C. Li
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Design Procedure ◽  
Solution Procedure ◽  
Bottom Level ◽  
Multi Level ◽  
Monotonicity Analysis ◽  
Optimization Based Design

Abstract This paper describes application of global monotonicity analysis within a decomposition framework. We present a general formulation and solution procedure, based on a bottom-level global monotornicity analysis, for a design optimization problem which is decomposed into three levels of subproblems. A well-known gear reducer example illustrates application of the method.

Multi-Level Design Optimization Using Global Monotonicity Analysis

1989 ◽  
Vol 111 (2) ◽  
pp. 259-263 ◽  
Author(s):  
S. Azarm ◽  
W.-C. Li
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Solution Procedure ◽  
Bottom Level ◽  
Optimization Framework ◽  
Multi Level ◽  
Level Design ◽  
Monotonicity Analysis

This paper describes application of global monotonicity analysis within a mutli-level design optimization framework. We present a general formulation and solution procedure, based on a bottom-level global monotonicity analysis, for a design optimization problem which is decomposed into three levels of subproblems. A well-known gear reducer example illustrates application of the method.

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

scholarly journals Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

2021 ◽  
Author(s):  
Yoshihiro Kanno
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Structural Reliability ◽  
Reliability Based Design Optimization ◽  
Input Distribution ◽  
Reliability Based Design ◽  
Optimal Value ◽  
Reliability Constraint ◽  
Distributionally Robust ◽  
Robust Reliability

AbstractThis study considers structural optimization under a reliability constraint, in which the input distribution is only partially known. Specifically, when it is only known that the expected value vector and the variance-covariance matrix of the input distribution belong to a given convex set, it is required that the failure probability of a structure should be no greater than a specified target value for any realization of the input distribution. We demonstrate that this distributionally-robust reliability constraint can be reduced equivalently to deterministic constraints. By using this reduction, we can handle a reliability-based design optimization problem under the distributionally-robust reliability constraint within the framework of deterministic optimization; in particular, nonlinear semidefinite programming. Two numerical examples are solved to demonstrate the relation between the optimal value and either the target reliability or the uncertainty magnitude.

A Literature Review of MADM Applications for Site Selection Problems — One Decade Review from 2011 to 2020

2021 ◽  
pp. 1-51
Author(s):  
Sarfaraz Hashemkhani Zolfani ◽  
Hamidreza Hasheminasab ◽  
Ali Ebadi Torkayesh ◽  
Edmundas Kazimieras Zavadskas ◽  
Arman Derakhti
Keyword(s):  
Site Selection ◽  
Environmental Planning ◽  
Optimization Problem ◽  
Residential Buildings ◽  
Field Of Study ◽  
Managerial Decision ◽  
Secondary Importance ◽  
Energy Projects ◽  
Number Of Publications ◽  
Project Level

Site selection is a multi-dimensional optimization problem that influences a wide variety of stakeholders from local communities and authorities to governments, environmental protection agencies, etc. Locating an energy project as well as transportation infrastructure projects, for instance, are of great strategic importance and are connected to the top-level regulations and policy levels. These problems are significant from strategic levels to the productivity of a single construction project level, from energy to transportation and from infrastructure to residential buildings. A large number of publications in this field of study prove this significance. However, regarding the variety of applications, managerial decision levels, and the growing number of studies, it is important to comprehend the latest trends and conclude an appropriate research path in this field. This study is mainly focused on the application of MADM methodologies on locating problems by which many studies are carried out and have a high coincidence with the locating problem environment. Consequently, 425 studies are considered in this study, and 217 more relevant papers are selected for the subsequent reviews. Based on the results, Energy projects are by far the most frequent field of study in this regard which are considered as renewable and nonrenewable categories. Also, environmental planning and sustainable site selection are of secondary importance.

Sequential Radial Basis Function-Based Optimization Method Using Virtual Sample Generation

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Yifan Tang ◽  
Teng Long ◽  
Renhe Shi ◽  
Yufei Wu ◽  
G. Gary Wang
Keyword(s):  
Radial Basis Function ◽  
Design Optimization ◽  
Basis Function ◽  
Optimization Problem ◽  
Optimization Method ◽  
Support Vector ◽  
Virtual Sample ◽  
Real Samples ◽  
Virtual Samples ◽  
Radial Basis

Abstract To further reduce the computational expense of metamodel-based design optimization (MBDO), a novel sequential radial basis function (RBF)-based optimization method using virtual sample generation (SRBF-VSG) is proposed. Different from the conventional MBDO methods with pure expensive samples, SRBF-VSG employs the virtual sample generation mechanism to improve the optimization efficiency. In the proposed method, a least squares support vector machine (LS-SVM) classifier is trained based on expensive real samples considering the objective and constraint violation. The classifier is used to determine virtual points without evaluating any expensive simulations. The virtual samples are then generated by combining these virtual points and their Kriging responses. Expensive real samples and cheap virtual samples are used to refine the objective RBF metamodel for efficient space exploration. Several numerical benchmarks are tested to demonstrate the optimization capability of SRBF-VSG. The comparison results indicate that SRBF-VSG generally outperforms the competitive MBDO methods in terms of global convergence, efficiency, and robustness, which illustrates the effectiveness of virtual sample generation. Finally, SRBF-VSG is applied to an airfoil aerodynamic optimization problem and a small Earth observation satellite multidisciplinary design optimization problem to demonstrate its practicality for solving real-world optimization problems.

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

Sign in / Sign up

Export Citation Format

Share Document