SMOOTHING APPROXIMATION TO l1EXACT PENALTY FUNCTION FOR CONSTRAINED OPTIMIZATION PROBLEMS

Smoothed Lower Order Penalty Function for Constrained Optimization Problems

The paper introduces a smoothing method to the lower order penalty function for constrained optimization problems. It is shown that, under some mild conditions, an optimal solution of the smoothed penalty problem is an approximate optimal solution of the original problem. Based on the smoothed penalty function, an algorithm is presented and its convergence is proved under some mild assumptions....

Constrained Evolutionary Optimization - the penalty function approach

Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems

Hessian Matrices of Penalty Functions for Solving Constrained-optimization Problems

This paper deals with the Hessian matrices of penalty functions, evaluated at their minimizing point. It is concerned with the condition number of these matrices for small values of a controlling parameter. At the end of the paper a comparison is made between different types of penalty functions on the grounds of the results obtained. 1. Classification of penalty-function techniques Throughout ...

Adaptive Penalty Function for Solving Constrained Evolutionary Optimization

A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. The penalty function approach is generic and applicable to any type of constraint (linear or nonlinear). Nonetheless, the most diffic...