\(\hbox {ACO}_{\mathbb {R}}\) is a well-established ant colony optimization algorithm that has been applied to neural network training. We present an approach for the dynamic adaptation of the \(\hbox {ACO}_{\mathbb {R}}\) algorithm’s search intensification/diversification parameter q, based on using several pre-specified parameter configurations, which we call personalities. Before an ant begins to generate a candidate solution, it stochastically adopts a personality based on the relative past success of the different personalities. The success of a personality is measured, in turn, by the relative quality of previous solutions generated by ants adopting that personality. The premise of our approach is that some personalities will be more appropriate than others for different phases of the search. This paper follows up on previous work which used a similar approach to adapting \(\hbox {ACO}_{\mathbb {R}}\) ’s search width parameter \(\xi\) . We evaluate our proposal experimentally in the context of training feedforward neural networks for classification using 65 benchmark datasets from the University of California Irvine (UCI) repository. Our experimental results indicate that our proposal produces better predictive accuracy than standard \(\hbox {ACO}_{\mathbb {R}}\) , to a statistically significant extent.
