Fig. 5 | Scientific Reports

Fig. 5

From: Hybrid prediction system for reliable multi-seasonal sustainable energy generation under meteorological and environmental volatility

Fig. 5Fig. 5Fig. 5

Optimization performance assessment results of the NCRBMO algorithm on the challenging IEEE CEC 2017 benchmark dataset. (a) Radar chart comparing NCRBMO against rival algorithms on the IEEE CEC benchmark functions. The benchmark comprises a total of 29 functions (F1-F30). Performance is evaluated according to the standard Friedman test’s mean ranking (positions 1–8), and a reduced radar area signifies better optimization results. (b)-(d) Convergence rate comparison for NCRBMO against its competitors on the IEEE CEC benchmark functions. The figure’s left panel displays the function types, while its right panel presents the convergence curve. Superior optimization performance is characterized by faster convergence and lower fitness values.

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