Ant colony optimization for mixed-variable optimization problems

摘要

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.