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A model-based predictive control strategy, aided by a differential discrete particle swarm optimization, is proposed. In particular, the proposed approach extends the traditional discrete binary version of the particle swarm algorithm by redesigning the particle section (all possible solutions in the search space) in order to represent a sequence of discrete controls. In this way, the “velocity section” turns out to be related to the probability of achieving the suitable discrete control value. As an experimental case study, this strategy is applied to a temperature control in a building aimed at energy saving. The proposed method is compared with a standard particle swarm algorithm, and experimental results are discussed.