Ant Colony Optimization for Mixed-Variable Optimization Problems

Abstract

In this paper, we introduce ACO _MV : an ant colony optimization (ACO) algorithm that extends the ACO _R algorithm for continuous optimization to tackle mixed-variable optimization problems. In ACO _MV , the decision variables of an optimization problem can be explicitly declared as continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACO _MV includes three solution generation mechanisms: a continuous optimization mechanism (ACO _R ), a continuous relaxation mechanism (ACO _MV -o) for ordinal variables, and a categorical optimization mechanism (ACO _MV -c) for categorical variables. Together, these mechanisms allow ACO _MV to tackle mixed-variable optimization problems. We also define a novel procedure to generate artificial, mixed-variable benchmark functions, and we use it to automatically tune ACO _MV 's parameters. The tuned ACO _MV is tested on various real-world continuous and mixed-variable engineering optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACO _MV on mixed-variable optimization problems.