This paper investigates an update strategy for the Univariate Marginal Distribution Algorithm (UMDA) probabilistic model inspired by the equations of the Ant Colony Optimization (ACO) computational paradigm. By adapting ACO's transition probability equations to the univariate probabilistic model, it is possible to control the balance between exploration and exploitation by tuning a single parameter. It is expected that a proper balance can improve the scalability of the algorithm on hard problems with bounded difficulties and experiments conducted on such problems with increasing difficulty and size confirmed these assumptions. These are important results because the performance is improved without increasing the complexity of the model, which is known to have a considerable computational effort.
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