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This work proposes a new trend for determining the numerical solutions of complex real life models by the profitable implementation of a nature inspired optimization technique, which is enthused by the pollination strategy of the flowering plants. The design methodology optimizes the determined residual fitness function achieved by the utilization of the generalized Taylor series for the deliberated fractional order differential model. Results of numerical experimentation achieved by the proposed pollination enthused residual optimization (PERO) technique are compared with some former numerical and metaheuristic techniques. Moreover, a detailed performance analysis is performed via statistical inference based on hundred independent runs.