Ant colony algorithm for a class of non-differentiable optimization problems

Abstract

There are many methods for solving non-differentiable optimization problems, but most of them are too difficult to realize. In this paper, penalty function method is adopted to transform non-differentiable optimization problems to unconstrained differentiable optimization problems. Then, computational experiments are conducted based on the uncertainty analysis of ant colony algorithm (ACA). Numerical results show that ACA can make such a problem simple and easy to calculate.