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Meta-heuristic optimization algorithms have gained popularity in solving complex, constrained optimization problems. The dynamic economic dispatch (DED) problem represents an example of such complex, constrained optimization problems. The aim of DED is to operate online units economically to meet the load demand, subjected to satisfying highly nonlinear and non-convex practical constraints. Therefore, it is possible that computational methods may not yield a global extremum as many local extrema may be encountered. This paper presents a novel constrained search-tactic to solve the DED problem. Two recently introduced meta-heuristic techniques, namely sensory-deprived optimization algorithm (SDOA) and artificial bee colony (ABC) algorithm, are adopted to evaluate the performance of the proposed constraint search-tactic. Two test systems are used to reveal the effectiveness of the offered tactic which successfully accelerates the employed algorithms' performance toward the optimal feasible region. After comparing the results, the outcomes when integrating the constrained search-tactic either outperformed or matched those obtained using other well-known methods.