A Stochastic Rank-Based Ant System for Discrete Structural Optimization

Penalty methods are often used to handle constraints in optimization problems. However, to find the optimal or near optimal set of penalty parameters is a hard task. Also, such values are problem dependent. This paper introduces the stochastic ranking approach to balance objective and penalty functions stochastically in a rank-based ACO metaheuristic. The results presented show that the simple inclusion of the procedure leads to an improved search performance, with respect to the standard penalty technique, when applied to discrete structural optimization problems