The Red Deer algorithm (RDA), a recently developed population-based meta-heuristic algorithm, is examined in this paper with the optimization task of complex functions. The RD algorithm blends evolutionary algorithms' survival of the fittest concept with heuristic search techniques' productivity and richness. It is critical to assess this algorithm's performance in comparison with other well-known heuristic methods. The findings are presented along with additional recommendations for increasing RDA performance based on the analysis. The readers of this paper will gain a grasp of the RD algorithm and its optimization ability to determine whether this algorithm is appropriate for their particular business, research, or industrial needs.