We use a primal-dual scheme to devise a new update rule for a penalty function method applicable to general optimization problems, including nonsmooth and nonconvex ones. The update rule we introduce uses dual information in a simple way. Numerical test problems show that our update rule has certain advantages over the classical one. We study the relationship between exact penalty parameters an...

Sufficient conditions are given for a classical dual exterior penalty function of a linear program to be independent of its penalty parameter. This ensures that an exact solution to the primal linear program can be obtained by minimizing the dual exterior penalty function. The sufficient conditions give a precise value to such a penalty parameter introduced in (Mangasarian, 2005), where no quan...

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...

Genetic Algorithms (GA) are, conceptually, suitable to optimize the Assembly Sequence Planning (ASP) problem. GA was implemented in this research to optimize the ASP problem because they can easily handle large search spaces, flexibility in defining the constraints and derive them in a fitness function. A penalty function approach has been used to compute the fitness value for assembly sequence...

In signal processing, high resolution signal parameter estimation is a significant problem. In particular the estimation of the direction of the narrow band signals emitted by multiple sources received wide applications recently in signal processing literature. Quite a number of papers appeared in the last twenty five years regarding the estimation of the parameters of the direction of arrival ...