In this paper we present an estimation of distribution par-ticle swarm optimization algorithm that borrows ideas from recent de-velopments in ant colony optimization. In the classical particle swarm optimization algorithm, particles exploit their individual memory to ex-plore the search space. However, the swarm as a whole has no means to exploit its collective memory (represented by the array of pbests) to guide its search. This causes a re-exploration of already known bad regions of the search space, wasting costly function evaluations. In our approach, we use the swarms collective memory to estimate the distribu-tion of promising regions in the search space and probabilistically guide the particles movement towards them. Our experiments show that this approach is able to find similar or better solutions than the standard particle swarm optimizer with fewer function evaluations.