Convergence Analysis of Bi-Attempted Based Optimization Algorithm for Numerical Global Optimum

Abstract

In this study, the search convergence properties of a recently developed Bi-Attempted Based Optimization Algorithm (ABaOA) on a six-hump camel function are demonstrated. The six-hump camel function, with its six local minima and two global minima, is one of the well-known fixed-dimension multimodal benchmark functions used to assess the effectiveness of optimization techniques. The ABaOA is intended to be tested on this benchmark function because real-world numerical optimization problems necessitate quick processing times. Results that are obtained from experiments are promising in terms of speed and viability. The highly effective search algorithm ABaOA ensures a workable solution while also quickly arriving at the global optimal solution.