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Optimal power flow (OPF) problem has already been attempted as a static problem, by adopting conventional methods and more recently, non-conventional ones, such as evolutionary algorithms (EAs) . However, as the loads and system topology in a power system vary time to time, these static-oriented methods are of limited use for this issue. More recently, as the emergence of another member of the EA family-bacterial foraging algorithm (BFA), the self- adaptability of individuals in the group searching activities has attracted a great deal of interests. Inspired from this algorithm, this paper presents a novel way to tackle the OPF problem from a non-static point of view. Based on the original BFA, an approach called dynamic bacterial foraging algorithm (DBFA) is proposed for the problem of OPF with dynamic loads. This approach has been examined and evaluated on the standard IEEE 30-bus test system. The simulation studies offer a range of changes in a dynamic environment. The simulation results show that DBFA can adapt to various environmental changes which occur in different probabilities, in comparison with a recent work on bacterial foraging  and particle swarm optimizer (PSO).