Tianjun Liao, Krzysztof Socha, Marco A. Montes de Oca Thomas Stützle, and Marco Dorigo
In this paper, we introduce ACOMV, an ant colony optimization (ACO)
algorithm for tackling 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. ACOMV includes 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 ACOMV 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 ACOMV's parameters. The tuned ACOMV is tested on various
real-world continuous and mixedvariable engineering optimization problems.
Comparisons with results from the literature demonstrate the effectiveness
and robustness of ACOMV on mixed-variable optimization problems.
Please look into the attached supplementary.pdf