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Fire hawk Optimizer (FHO) is a relatively new intake in the family of evolutionary algorithms for a distinct type of optimization problem. Initialization of the population plays a significant role in solving classical optimization issues. Incorporating quasi-random sequences such as the sobol, halton, and torus sequences, this study demonstrates novel ways for swarm initiation. The outcomes of our proposed techniques display outstanding performance as compared with the traditional FHO. The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the standard approach. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.