The Flamingo Search Algorithm (FSA) is a recently developed Nature-Inspired (NI) metaheuristic optimization algorithm inspired by flamingos migrating movement and foraging behaviour. FSA has depicted fine improvements in the convergence speed and solution quality of many standard NI algorithms. Since many nature-inspired algorithms have shown improvements in results by hybridizing their exploration or exploitation heuristics with other NI heuristics, this paper proposes a novel hybridized Shuffled Flamingo Search Algorithm (ShFSA) which combines the exploration pattern of FSA with exploitation pattern of SFLA (Shuffled Frog Leaping Algorithm). The proposed algorithm is tested on various unimodal, multi-modal and fixed dimensional bench marks functions to validate the efficiency. Detailed comparative analysis of generated results demonstrates that hybridized ShFSA has shown significant improvement in convergence speed and solution quality as compared to traditional FSA in number of standard functions.