MRBMO: An Enhanced RBMO Algorithm For Solving Numerical Optimization Challenges

To solve the limitation of easily getting trapped in local optima and slow convergence rates of Red-billed Blue Magpie Optimization algorithm (RBMO), an enhanced RBMO algorithm (MRBMO) was proposed. MRBMO was improved by integrating Good Nodes Set Initialization, an Enhanced Search-for-food Strategy, a newly-designed Siege-style Attacking-prey Strategy and Lens-Imaging Opposition-Based Learning (LIOBL). The experimental results show that MRBMO is superior to traditional algorithms in terms of convergence speed and solving accuracy, especially in high-dimensional search space. This paper designs two types of simulation experiments to test the practicability of MRBMO. First, MRBMO is used along with other heuristic algorithms to solve four engineering design optimization problems, aiming to verify the applicability of MRBMO in engineering design optimization. Then, to overcome the shortcomings of metaheuristic algorithms in antenna S-parameter optimization problems, such as time-consuming verification processes, cumbersome operations, and complex modes, this paper adopts a test suite specifically designed for antenna S-parameter optimization, with the goal of efficiently validating the effectiveness of metaheuristic algorithms in antenna S-parameter optimization. The results show that MRBMO demonstrates significant advantages in both engineering design optimization and antenna S-parameter optimization.