Extremal Behavior of One Parameter Families of Optimal Design Models

1989 ◽  
Author(s):  
J. R. J. Rao ◽  
P. Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Parametric Optimization ◽  
Optimal Solution ◽  
Engineering System ◽  
Design Models ◽  
Numerical Examples ◽  
Mathematical Properties ◽  
Extremal Behavior ◽  
Rigorous Treatment ◽  
Multi Level

Abstract Optimal design models contain parameters that are considered fixed during the optimization process. When these parameters change values, the mathematical properties of the model and the physical behavior of the underlying engineering system may change drastically. This article examines how large changes in one parameter affect the optimal solution and the type of singularities that may be encountered. The theory presented extends parametric optimization beyond the usual sensitivity analysis, and can be used as a modeling tool or as a rigorous treatment of related problems, such as multi-level decomposition. The algorithmic implementation and numerical examples are presented in a sequel article.

Configuration Robustness Analysis of the Optimal Design of Cable-Driven Manipulators

2016 ◽  
Vol 8 (6) ◽  
Author(s):  
Joshua T. Bryson ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Optimal Design ◽  
Stochastic Optimization ◽  
High Dimension ◽  
Degrees Of Freedom ◽  
Robustness Analysis ◽  
Optimal Solution ◽  
Design Parameters ◽  
Robot Leg ◽  
Robot Performance

Designing an effective cable architecture for a cable-driven robot becomes challenging as the number of cables and degrees of freedom of the robot increase. A methodology has been previously developed to identify the optimal design of a cable-driven robot for a given task using stochastic optimization. This approach is effective in providing an optimal solution for robots with high-dimension design spaces, but does not provide insights into the robustness of the optimal solution to errors in the configuration parameters that arise in the implementation of a design. In this work, a methodology is developed to analyze the robustness of the performance of an optimal design to changes in the configuration parameters. This robustness analysis can be used to inform the implementation of the optimal design into a robot while taking into account the precision and tolerances of the implementation. An optimized cable-driven robot leg is used as a motivating example to illustrate the application of the configuration robustness analysis. Following the methodology, the effect on robot performance due to design variations is analyzed, and a modified design is developed which minimizes the potential performance degradations due to implementation errors in the design parameters. A robot leg is constructed and is used to validate the robustness analysis by demonstrating the predicted effects of variations in the design parameters on the performance of the robot.

Optimal Design of Power System Stabilizer Using a Novel Evolutionary Algorithm

2018 ◽  
Vol 7 (3) ◽  
pp. 24-46
Author(s):  
Sourav Paul ◽  
Provas Roy
Keyword(s):  
Optimal Design ◽  
Power System ◽  
Search Algorithm ◽  
Low Frequency ◽  
Optimal Solution ◽  
Power System Stabilizer ◽  
Low Frequency Oscillation ◽  
Large Power ◽  
Wide Range ◽  
System Stabilizer

In this article, an Oppositional Differential search algorithm (ODSA) is comprehensively developed and successfully applied for the optimal design of power system stabilizer (PSS) parameters which are added to the excitation system to dampen low frequency oscillation as it pertains to large power system. The effectiveness of the proposed method is examined and validated on a single machine infinite bus (SMIB) using the Heffron-Phillips model. The most important advantage of the proposed method is as it reaches toward the optimal solution without the optimal tuning of input parameters of the ODSA algorithm. In order to verify the effectiveness, the simulation was made for a wide range of loading conditions. The simulation results of the proposed ODSA are compared with those obtained by other techniques available in the recent literature to demonstrate the feasibility of the proposed algorithm.

scholarly journals Smoothing Approximation to the Square-Root Exact Penalty Function

2016 ◽  
Vol 4 (1) ◽  
pp. 87-96
Author(s):  
Yaqiong Duan ◽  
Shujun Lian
Keyword(s):  
Penalty Function ◽  
Original Problem ◽  
Optimal Solution ◽  
Penalty Functions ◽  
Exact Penalty Functions ◽  
Exact Penalty ◽  
Square Root ◽  
Inequality Constrained Optimization ◽  
Numerical Examples ◽  
Mild Conditions

AbstractIn this paper, smoothing approximation to the square-root exact penalty functions is devised for inequality constrained optimization. It is shown that an approximately optimal solution of the smoothed penalty problem is an approximately optimal solution of the original problem. An algorithm based on the new smoothed penalty functions is proposed and shown to be convergent under mild conditions. Three numerical examples show that the algorithm is efficient.

Optimal Design of Plastic Structures for Fixed and Shakedown Loadings

1973 ◽  
Vol 40 (2) ◽  
pp. 595-599 ◽  
Author(s):  
M. Z. Cohn ◽  
S. R. Parimi
Keyword(s):  
Optimal Design ◽  
Loading Condition ◽  
Minimum Weight ◽  
Optimal Solution ◽  
Failure Criteria ◽  
Optimal Designs ◽  
Design Solution ◽  
Variable Loading ◽  
Framed Structures ◽  
A Value

Optimal (minimum weight) solutions for plastic framed structures under shakedown conditions are found by linear programming. Designs that are optimal for two failure criteria (collapse under fixed loads and collapse under variable repeated loads) are then investigated. It is found that these designs are governed by the ratio of the specified factors defining the two failure criteria, i.e., for shakedown, λs and for collapse under fixed loading, λ. Below a certain value (λs/λ)min the optimal solution under fixed loading is also optimal for fixed and shakedown loading. Above a value (λs/λ)max the optimal design for variable loading is also optimal under the two loading conditions. For intermediate values of λs/λ the optimal design that simultaneously satisfies the two criteria is different from the optimal designs for each independent loading condition. An example illustrates the effect of λs/λ on the nature of the design solution.

Discrete Optimal Design Formulations: Application to Gear Train Design

1992 ◽  
Author(s):  
Leonard P. Pomrehn ◽  
Panos Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Spur Gear ◽  
Gear Train ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Interesting Aspect ◽  
Model Formulation ◽  
Design Models ◽  
Different Types

Abstract The use of discrete variables in optimal design models offers the opportunity to deal rigorously with an expanded variety of design situations, as opposed to using only continuous variables. However, complexity and solution difficulty increase dramatically and model formulation becomes very important. A particular problem arising from the design of a gear train employing four spur gear pairs is introduced and formulated in several different ways. An interesting aspect of the problem is its exhibition of three different types of discreteness. The problem could serve as a test for a variety of optimization or artificial intellegence techniques. The best known solution is included in this article, while its derivation is given in a sequel article.

Implementation of Semi-Heuristic Reasoning for Boundedness Analysis of Design Optimization Models

1987 ◽  
Author(s):  
J. R. J. Rao ◽  
P. Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Heuristic Search ◽  
Optimization Models ◽  
Programming Environment ◽  
Initial Model ◽  
Active Set ◽  
Design Models ◽  
Active Set Strategy ◽  
Reasoning System ◽  
Global Boundedness

Abstract A production system performing global boundedness analysis of optimal design models has been implemented in the OPS5 programming environment. The system receives as input an initial model monotonicity table and derives global facts about boundedness and constraint activity using monotonicity principles. Additional facts may be discovered by heuristic search of implicit elimination sequences that examine boundedness of reduced models with active constraints eliminated. The global facts generated automatically by this reasoning system can be used either for a global solution, or for a combined local-global active set strategy.

Decomposition Procedure for Solving NLP and QP Problems based on Lagrange and Sander's Method

2016 ◽  
Vol 7 (4) ◽  
pp. 67-93
Author(s):  
H. K. Das
Keyword(s):  
Linear Programming ◽  
Optimal Solution ◽  
Computational Technique ◽  
Lagrange Method ◽  
Computer Technique ◽  
Numerical Examples ◽  
Non Linear Programming ◽  
Decomposition Procedure ◽  
Concave Quadratic Programming ◽  
Modified Algorithm

This paper develops a decompose procedure for finding the optimal solution of convex and concave Quadratic Programming (QP) problems together with general Non-linear Programming (NLP) problems. The paper also develops a sophisticated computer technique corresponding to the author's algorithm using programming language MATHEMATICA. As for auxiliary by making comparison, the author introduces a computer-oriented technique of the traditional Karush-Kuhn-Tucker (KKT) method and Lagrange method for solving NLP problems. He then modify the Sander's algorithm and develop a new computational technique to evaluate the performance of the Sander's algorithm for solving NLP problems. The author observe that the technique avoids some certain numerical difficulties in NLP and QP. He illustrates a number of numerical examples to demonstrate his method and the modified algorithm.

scholarly journals Inventory Models with Defective Units and Sub-Lot Inspection

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1038
Author(s):  
Han-Wen Tuan ◽  
Gino K. Yang ◽  
Kuo-Chen Hung
Keyword(s):  
Lower Bound ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Interior Solution ◽  
Order Quantity ◽  
Inventory Models ◽  
Defective Items ◽  
Numerical Examples ◽  
Counter Example ◽  
Analytical Foundation

Inventory models must consider the probability of sub-optimal manufacturing and careless shipping to prevent the delivery of defective products to retailers. Retailers seeking to preserve a reputation of quality must also perform inspections of all items prior to sale. Inventory models that include sub-lot sampling inspections provide reasonable conditions by which to establish a lower bound and a pair of upper bounds in terms of order quantity. This should make it possible to determine the conditions of an optimal solution, which includes a unique interior solution to the problem of an order quantity satisfying the first partial derivative. The approach proposed in this paper can be used to solve the boundary. These study findings provide the analytical foundation for an inventory model that accounts for defective items and sub-lot sampling inspections. The numerical examples presented in a previous paper are used to demonstrate the derivation of an optimal solution. A counter-example is constructed to illustrate how existing iterative methods do not necessarily converge to the optimal solution.

scholarly journals A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates

2019 ◽  
Vol 9 (20) ◽  
pp. 4267
Author(s):  
Chien Yang Huang ◽  
Tai Yan Kam
Keyword(s):  
Optimal Design ◽  
Elastic Constants ◽  
Composite Laminates ◽  
Composite Laminate ◽  
Optimization Problem ◽  
Optimization Method ◽  
Global Minimization ◽  
Minimization Problems ◽  
Design Variables ◽  
Multi Level

A new and effective elastic constants identification technique is presented to extract the elastic constants of a composite laminate subjected to uniaxial tensile testing. The proposed technique consists of a new multi-level optimization method that can solve different types of minimization problems, including the extraction of material constants of composite laminates from given strains. In the identification process, the optimization problem is solved by using a stochastic multi-start dynamic search minimization algorithm at the first level in order to obtain the statistics of the quasi-optimal design variables for a set of randomly generated starting points. The statistics of the quasi-optimal elastic constants obtained at this level are used to determine the reduced feasible region in order to formulate the second-level optimization problem. The second-level optimization problem is then solved using the particle swarm algorithm in order to obtain the statistics of the new quasi-optimal elastic constants. The iteration process between the first and second levels of optimization continues until the standard deviations of the quasi-optimal design variables at any level of optimization are less than the prescribed values. The proposed multi-level optimization method, as well as several existing global optimization algorithms, is used to solve a number of well-known mathematical minimization problems to verify the accuracy of the method. For the adopted numerical examples, it has been shown that the proposed method is more efficient and effective than the adopted global minimization algorithms to produce the exact solutions. The proposed method is then applied to identify four elastic constants of a [0°/±45°]s composite laminate using three strains in 0°, 45°, and 90° directions, respectively, of the composite laminate subjected to uniaxial testing. For comparison purposes, several existing global minimization techniques are also used to solve the elastic constants identification problem. Again, it has been shown that the proposed method is capable of producing more accurate results than the adopted available methods. Finally, experimental data are used to demonstrate the applications of the proposed method.

Hydrograph-based storm sewer design optimization by genetic algorithm

2006 ◽  
Vol 33 (3) ◽  
pp. 319-325 ◽  
Author(s):  
M H Afshar ◽  
A Afshar ◽  
M A Mariño ◽  
A A.S Darbandi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Environmental Protection Agency ◽  
Optimal Solution ◽  
Storm Water ◽  
Storm Water Management Model ◽  
Decision Variables ◽  
Proposed Model ◽  
The Us ◽  
Hydraulic Simulator

A model is developed for the optimal design of storm water networks. The model uses a genetic algorithm (GA) as the search engine and the TRANSPORT module of the US Environmental Protection Agency storm water management model version 4.4H (SWMM4.4H) as the hydraulic simulator. Two different schemes are used to formulate the problem with varying degrees of success in reaching a near-optimal solution. In the first scheme, the nodal elevations and pipe diameters are selected as the decision variables of the problem which were determined by the GA to produce the trial solutions. In the second scheme, only nodal elevations are optimized by the GA, and determination of pipe diameters is left to the TRANSPORT SWMM module. Simulation of the trial solutions in both methods is carried out by the TRANSPORT module of SWMM4.4H. The proposed model is applied to some benchmark examples, and the results are presented and compared with the existing results in the literature.Key words: genetic algorithm, optimal design, sewer network, SWMM.

Sign in / Sign up

Export Citation Format

Share Document