Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization

In this paper, we propose an extension of Self-adaptive Differential Evolution algorithm (SaDE) to solve optimization problems with constraints. In comparison with the original SaDE algorithm, the replacement criterion was modified for handling constraints. The performance of the proposed method is reported on the set of 24 benchmark problems provided by CEC2006 special session on constrained real parameter optimization.

I. Introduction

Many optimization problems in science and engineering have a number of constraints. Evolutionary algorithms have been successful in a wide range of applications. However, evolutionary algorithms naturally perform unconstrained search. Therefore, when used for solving constrained optimization problems, they require additional mechanisms to handle constraints in their fitness function. In the literature, several constraints handling techniques have been suggested for solving constrained optimization by using evolutionary algorithms.