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Ant colony optimization for mixed-variable optimization problems

par Liao, Tianjun ;Socha, Krzysztof ;Montes De Oca Roldan, Marco ;Stützle, Thomas ;Dorigo, Marco
Référence IEEE transactions on evolutionary computation, 18, 4, page (503-518)
Publication Publié, 2014

Article révisé par les pairs

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Résumé :

In this paper, we introduce ACOMV : an ant colony optimization (ACO) algorithm that extends the ACOℝ algorithm 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. 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 ACO MV 's parameters. The tuned ACOMV 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 ACOMV on mixed-variable optimization problems. © 1997-2012 IEEE.