scholarly journals A new logarithmic penalty function approach for nonlinear constrained optimization problem

2019 ◽  
pp. 353-362 ◽  
Author(s):  
Mansur Hassan ◽  
Adam Baharum
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problem ◽  
Function Approach ◽  
Constrained Optimization Problem ◽  
Nonlinear Constrained Optimization ◽  
Penalty Function Approach

scholarly journals A Novel Penalty Function Approach to Constrained Optimization Problems with Genetic Algorithms

1998 ◽  
Vol 2 (6) ◽  
pp. 208-213 ◽  
Author(s):  
Xinghuo Yu ◽  
◽  
Weixing Zheng ◽  
Baolin Wu ◽  
Xin Yao ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Unconstrained Optimization ◽  
Penalty Function ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Function Approach ◽  
Constrained Optimization Problem ◽  
Constrained Optimization Problems ◽  
Unconstrained Optimization Problem ◽  
Penalty Function Approach

In this paper, a novel penalty function approach is proposed for constrained optimization problems with linear and nonlinear constraints. It is shown that by using a mapping function to "wrap" up the constraints, a constrained optimization problem can be converted to an unconstrained optimization problem. It is also proved mathematically that the best solution of the converted unconstrained optimization problem will approach the best solution of the constrained optimization problem if the tuning parameter for the wrapping function approaches zero. A tailored genetic algorithm incorporating an adaptive tuning method is then used to search for the global optimal solutions of the converted unconstrained optimization problems. Four test examples were used to show the effectiveness of the approach.

Optimum Synthesis of Path-Generating Four-Bar Mechanisms

1975 ◽  
Vol 97 (1) ◽  
pp. 314-321 ◽  
Author(s):  
N. Bakthavachalam ◽  
J. T. Kimbrell
Keyword(s):  
Penalty Function ◽  
Gradient Method ◽  
Optimization Problem ◽  
Inequality Constraints ◽  
Unconstrained Minimization ◽  
Function Approach ◽  
Optimum Synthesis ◽  
Penalty Function Approach

Synthesis of path-generating four-bar mechanisms is considered as an optimization problem under inequality constraints. The penalty function approach is used. The effects of clearances and tolerances in manufacture are considered in order to make sure that the inequality constraints are within the acceptable tolerance during the required motion. Modifications are introduced in the gradient method, and sequential unconstrained minimization techniques are used in the process of minimization. A typical example under various conditions is presented in order to study the effectiveness of the technique.

An Artificial Evolutionary Approach for Solving the Nonlinear Constrained Optimization Problems

2013 ◽  
Vol 479-480 ◽  
pp. 861-864
Author(s):  
Yi Chih Hsieh ◽  
Peng Sheng You
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Approach ◽  
Benchmark Problems ◽  
Second Phase ◽  
Constrained Optimization Problem ◽  
Constrained Optimization Problems ◽  
Two Phase ◽  
Nonlinear Constrained Optimization

In this paper, an artificial evolutionary based two-phase approach is proposed for solving the nonlinear constrained optimization problems. In the first phase, an immune based algorithm is applied to solve the nonlinear constrained optimization problem approximately. In the second phase, we present a procedure to improve the solutions obtained by the first phase. Numerical results of two benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature.

Sign in / Sign up

Export Citation Format

Share Document