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In this paper, we show how ant colony optimization (ACO) may be used for tackling mixed-variable optimization problems. We show how a version of ACO extended to continuous domains (ACO R ) may be further used for mixed-variable problems. We present different approaches to handling mixed-variable optimization problems and explain their possible uses. We propose a new mixed-variable benchmark problem. Finally, we compare the results obtained to those reported in the literature for various real-world mixed-variable optimization problems.

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.