Levy Flight Based Fire Hawk Optimizer

This paper introduces the Levy Flight based Fire Hawk Optimizer (LFHO), an enhanced variant of the Fire Hawk Optimizer (FHO). The idea for LFHO emerged from observing the limitations in the original FHO, notably its re-stricted exploration capability and its tendency to get trapped in local optima, leading to sub-optimal solutions. Our motivation was to overcome these limitations and enhance the algorithm's performance in multifaceted problems. LFHO incorporates a random walk mechanism to inject stochasticity into the search process, thus broadening the exploration scope and mitigating premature convergence issues. Additionally, the introduction of dual global best solutions facilitates a more comprehensive exploration of the solution space, while a dynamic re-initialization mechanism for global best solutions ensures adaptability to evolving search landscapes. Furthermore, a refined nearest function enhances navigation towards promising solutions with greater accuracy and efficiency. The LFHO algorithm's performance is evaluated on CEC(Congress on Evolutionary Computation) 2014 benchmark functions, demonstrating significant improvements over the original FHO in addressing complex optimization tasks.