A Performance-Driven Enhancement of the Mud Ring Algorithm for Global Optimization Challenges

Nowadays, real-world optimization problems are becoming increasingly complex tasks, prompting the development of nature-inspired algorithms that mimic biological phenomena to improve search performance and solution quality. The Mud Ring Algorithm (MRA), inspired by the cooperative hunting behavior of bottlenose dolphins, has shown promise but remains sensitive to parameter settings, especially when balancing exploration and exploitation. To address these limitations, this paper proposes the Enhanced Mud Ring Algorithm (EMRA), which introduces a novel mechanism to more effectively manage the exploration-exploitation tradeoff. This modification allows the algorithm to escape local minima and explore the solution space more effectively. Numerical experiments on some standard benchmark functions as well as the difficult Congress on Evolutionary Computation 2019 benchmark suite show that EMRA outperforms the original MRA in terms of accuracy performance and computational cost, especially when dealing with high-dimension and multi-peak functions. In addition, EMRA was used in three complex engineering optimization problem designs (welded beam, pressure vessel, and tension spring), and the results were found to be more accurate and reliable than MRA. These results validate the strength and applicability of EMRA as a general optimization tool to tackle complex problems from many different disciplines, including real-world problems requiring the exhaustive exploration of multiple options. In summary, this study shows that EMRA is an effective extension of metaheuristic optimization that is applicable in real-world problems.