Design of Reactionless Mechanisms Based on Constrained Optimization Procedure

2016 ◽  
pp. 273-298
Author(s):  
Himanshu Chaudhary ◽  
Kailash Chaudhary
Keyword(s):  
Constrained Optimization ◽  
Optimization Procedure

scholarly journals Constrained optimization of line-start PM motor based on the gray wolf optimizer

2021 ◽  
Vol 23 (1) ◽  
pp. 1-10
Author(s):  
Łukasz Knypiński
Keyword(s):  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Computer Software ◽  
Optimization Procedure ◽  
Bat Algorithm ◽  
Linear Constraint ◽  
Gray Wolf ◽  
Good Convergence ◽  
Design Variables ◽  
Pm Synchronous Motor

This paper presents the algorithm and computer software for constrained optimization based on the gray wolf algorithm. The gray wolf algorithm was combined with the external penalty function approach. The optimization procedure was developed using Borland Delphi 7.0. The developed procedure was then applied to design of a line-start PM synchronous motor. The motor was described by three design variables which determine the rotor structure. The multiplicative compromise function consisted of three maintenance parameters of designed motor and one non-linear constraint function was proposed. Next, the result obtained for the developed procedure (together with the gray wolf algorithm) was compared with results obtained using: (a) the particle swarm optimization algorithm, (b) the bat algorithm and (c) the genetic algorithm. The developed optimization algorithm is characterized by good convergence, robustness and reliability. Selected results of the computer simulation are presented and discussed.

scholarly journals Box-constrained optimization for minimax supervised learning

2021 ◽  
Vol 71 ◽  
pp. 101-113
Author(s):  
Cyprien Gilet ◽  
Susana Barbosa ◽  
Lionel Fillatre
Keyword(s):  
Constrained Optimization ◽  
Empirical Bayes ◽  
Optimization Procedure ◽  
Affine Function ◽  
Bayes Risk ◽  
Piecewise Affine ◽  
Empirical Risk ◽  
Polyhedral Domain ◽  
Subgradient Algorithm ◽  
Constrained Minimax

In this paper, we present the optimization procedure for computing the discrete boxconstrained minimax classifier introduced in [1, 2]. Our approach processes discrete or beforehand discretized features. A box-constrained region defines some bounds for each class proportion independently. The box-constrained minimax classifier is obtained from the computation of the least favorable prior which maximizes the minimum empirical risk of error over the box-constrained region. After studying the discrete empirical Bayes risk over the probabilistic simplex, we consider a projected subgradient algorithm which computes the prior maximizing this concave multivariate piecewise affine function over a polyhedral domain. The convergence of our algorithm is established.

NEWSUMT-A: A General Purpose Program for Constrained Optimization Using Constraint Approximations

1985 ◽  
Vol 107 (1) ◽  
pp. 94-99 ◽  
Author(s):  
R. V. Grandhi ◽  
R. Thareja ◽  
R. T. Haftka
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Procedure ◽  
General Purpose ◽  
Constrained Optimization Problems ◽  
Element Analysis ◽  
Viable Solution ◽  
Order Constraint ◽  
Smooth Convergence ◽  
Displacement Constraints

The solution of complex constrained optimization problems is often prohibitively expensive because of the computational expense of evaluating the constraints. Under these circumstances the only viable solution is to employ inexpensive constraint approximations accompanied by move-limits that guard against large errors in the approximated constraints. The NEWSUMT-A program is a general purpose optimization program which automates the process of selecting constraint approximations and move-limits to ensure smooth convergence. The present paper describes the theoretical basis and the implementation of the optimization procedure. Examples from the field of structural optimization are presented. These involve stress and displacement constraints which require a costly finite element analysis. Additionally, the performance of two first-order constraint approximations is compared.

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

Sign in / Sign up

Export Citation Format

Share Document