Ant Colony Optimization for Mixed-Variable Optimization Problems

作者：Liao Tianjun^*; Socha Krzysztof; de Oca Marco A Montes; Stuetzle Thomas; Dorigo Marco

来源：IEEE Transactions on Evolutionary Computation, 2014, 18(4): 503-518.

DOI：10.1109/TEVC.2013.2281531

• Ant colony optimization
• artificial mixed-variable benchmark functions
• automatic parameter tuning
• engineering optimization
• mixed-variable optimization problems

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)%26apos;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.

• 出版日期2014-8