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In this paper, the short-term hydro-thermal scheduling (STHTS) optimization problem is treated considering the environmental aspects. An improved bacterial foraging algorithm (IBFA) is implemented to solve this bi-objective problem. In addition to minimizing the cost function, the minimization of nitrogen oxides (NOx) is also considered. The environmentally constrained STHTS problem, as it is the case of the classic one, is a dynamic large-scale nonlinear optimization problem which requires solving unit commitment and economic power load dispatch problems. The bacterial foraging algorithm (BFA) is a recently developed evolutionary optimization technique based on the foraging behavior of the E. coli bacteria. The BFA has been successfully employed to solve various optimization problems; however, for large-scale problems such as the STHTS problem, it shows poor convergence properties. To tackle this complex problem considering its high-dimensioned search space, significant improvements are introduced to the basic BFA. The algorithm is validated using a well known hydro-thermal generation system. Results are obtained with the shape of the Pareto-optimal front and the trade-off set of solutions are successfully captured.