A self-adaptive interior penalty based differential evolution algorithm for constrained optimization

Abstract

A self-adaptive interior penalty method is proposed for the constrained optimization problems by using interior penalty method to handle constraints. A set of interior penalty rules are designed to evaluate feasible solutions and infeasible solutions separately. A self-adaptive penalty factor method is proposed to prevent the interior penalty method from being sensitive to the values of penalty factor and to minimize the interior penalty function value of the optimal solution. As an instance of implementation, a different evolution algorithm is improved by means of the method proposed in this paper, based on which 10 benchmark problems are tested. The numerical solution results indicate that the performance of the method is better than four existing state-of-the-art techniques.