Improvisation Of The Nature-Inspired Meta-Heuristic Algorithm For Optimized Clustering

The Hunger Game Search (HGS) algorithm is very effective when it comes to finding the best answer. This research article proposes a Lévy Flight and Cauchy Flight enhancement to the hunger game search (HGS) method, which makes the HGS quicker and more resilient while avoiding premature convergence. It also helps to promote population variety, which protects against early convergence and improves the capacity to leap out of local optima. This technique aids in achieving a better balance between HGS exploration and exploitation. Rapid convergence and high accuracy characterize the proposed method, and it can effectively eliminate a local optimum. The Usability of HGS was further confirmed by comparing it with a variety of conventional and exceptional algorithms with 28 known optimization functions, as well as the IEEE CEC 2014 benchmark test suite. The usability of this method is further exemplified by its application to a variety of technical problems. In addition, it showed the proposed algorithm with higher identification costs of the simulation research results opposite other algorithms. All findings support the effectiveness of the targeted optimizer and exemplify the superiority of the HSA algorithm.