Constrained optimization using penalty function method combined with genetic algorithm

Abstract

In the paper the way of adaptation of the penalty function method to the genetic algorithm is presented. In case of application of the external penalty function, the penalty term may exceed the value of the primary objective function. This means, that the value of the modified objective function is negative, while in genetic algorithm the adaptation must be of positive value, especially it in the selection procedure utilizes the roulette method. The sigmoidal transformation is applied to solve this problem. The computer software is developed in the Delphi environment. The proposed approach is applied to optimization of the electromagnetic linear actuator.