DocumentCode :
5023
Title :
Ant Colony Optimization for Mixed-Variable Optimization Problems
Author :
Tianjun Liao ; Socha, Krzysztof ; Montes de Oca, Marco A. ; Stutzle, Thomas ; Dorigo, Marco
Author_Institution :
Inst. de Rech. Interdisciplinaires et de Developpements en Intell. Artificielle, Univ. Libre de Bruxelles, Brussels, Belgium
Volume :
18
Issue :
4
fYear :
2014
fDate :
Aug. 2014
Firstpage :
503
Lastpage :
518
Abstract :
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 ACOMV´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.
Keywords :
ant colony optimisation; ACOR algorithm; ant colony optimization; automatic ACOMV parameter tuning; categorical optimization mechanism; continuous optimization mechanism; continuous relaxation mechanism; decision variables; mixed-variable benchmark functions; mixed-variable engineering optimization problems; Benchmark testing; Equations; Indexes; Linear programming; Mathematical model; Optimization; Probabilistic logic; Ant colony optimization; artificial mixed-variable benchmark functions; automatic parameter tuning; engineering optimization; mixed-variable optimization problems;
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/TEVC.2013.2281531
Filename :
6595550
Link To Document :
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