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Ant Colony Optimization for Mixed-Variable Optimization Problems
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DOI：10.1109/TEVC.2013.2281531.png)
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Abstract
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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.
Keywords:
Ant colony optimization
artificial mixed-variable benchmark functions
automatic parameter tuning
engineering optimization
mixed-variable optimization problems
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