A study of adaptive penalty functions for constrained genetic algorithm-based optimization

Abstract

Several potential approaches are presented that utilize adaptive penalty functions that change the value of the draw-down coefficients during a run of the genetic algorithm. A simple 1D constrained problem and a more complex 2D constrained problem were solved using the adaptive penalty strategies. A stiffened composite panel was optimized for minimum weight, subject to several constraints using the adaptive penalty methods to provide insight into how the approaches perform on an engineering problem. On the basis of these problem solutions, conclusions were drawn regarding the efficacy of adaptive penalty functions for constrained optimization. (Author)