Ant Colony Optimization for Mixed-Variable Optimization Problems
Tianjun Liao, Krzysztof Socha, Marco A. Montes de, Thomas Stützle, Marco Dorigo · IEEE Transactions on Evolutionary Computation · 2013
In this paper, we introduce ACOMV: an ant colony optimization (ACO) algorithm that extends the ACORalgorithm for continuous optimization to tackle mixed-variable optimization problems. In ACOMV, the decision variables of an optimization problem can be explicitly declared as continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACOMVincludes three solution generation mechanisms: a continuous optimization mechanism (ACOR), a continuous relaxation mechanism (ACOMV-o) for ordinal variables, and a categorical optimization mechanism (ACOMV-c) for categorical variables. Together, these mechanisms allow ACOMVto 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 ACOMV's parameters. The tuned ACOMVis tested on various real-world continuous and mixed-variable engineering optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACOMVon mixed-variable optimization problems.