An Improved Differential Evolution Algorithm for Solving Constrained Optimization Problems

Abstract

This article presents an improved differential evolution algorithm for solving constrained optimization problems. In the algorithm, the dynamically relaxing the constraint violation tolerance is given to balance the global search ability and the local search ability and to dynamically guide the individuals to tend to the feasible region. In addition, a new returning technique is used to ensure that the mutated individuals are all in the search space. It is shown by the numerical results that the proposed algorithm is effective and robust and has good global optimization ability.