Performance investigation between the Newton-Raphson-based optimizer and the recently proposed meta-heuristic algorithms on different theoretical optimization problems

This study presents a detailed performance analysis of a recently proposed meta-heuristic algorithm, the Newton-Raphson-based optimizer (NRBO), across various theoretical optimization problems, compared to other state-of-the-art algorithms. In particular, NRBO is implemented to address four testing problems widely used in the development of most modern meta-heuristic algorithms. Then the results achieved by NRBO are compared with those of two other recently introduced methods, including the Arctic Puffin Optimizer (APO) and the Sharpbelly Fish Optimization (SFO). The comparison results across four criteria indicate that NRBO outperforms the other two in terms of convergence speed, the ability to reach the best fitness values, and, more importantly, the standard deviation (STD). In particular, NRBO consistently outperforms APO and SFO on the Minimum, mean, and maximum fitness values across the four testing problems, with differences in the space solution, variable constraints, and overall complexity. Based on the comparison results and proven capability, NRBO is considered an effective and reliable search method, strongly recommended for use in dealing with such theoretical optimization problems.