Musk Ox optimizer with Enhanced Exploration and Exploitation Capabilities for Cognitive Radio System Design

Musk Ox Optimizer (MO) is a metaheuristic based upon the social behavior of musk ox populations, which depend upon their population-wide defensive, feeding and migrating decisions based on signals provided by the "guards" within the population. It has found application use in several scientific and engineering applications with success. The present study will enhance the effectiveness of the classic MO algorithm. Its ability to innovate and exploit potentially new solutions is also aimed at enhancing its performance. This work suggests an improved MO algorithm to strike the balance between exploitation and exploration potential to attain an improved performance of traditional MO. The exploration capabilities of MO have been upgraded by the use of the Elite Opposition Based Learning (EOBL) approach. Local Neighborhood Search (LNS) has also enhanced its exploitation capabilities, inspired by the experience of the best solution observed in a local neighborhood of the current solution. The suggested approach is tested with the CEC2019 benchmark functions, and the computational results indicate that it provides promising results. Finally, to optimize the allocation of frequency spectrum, the proposed algorithm has been applied on the cognitive radio (CR) system to indicate its potential in solving real-life situations. It has been demonstrated through experimentation and statistical analysis that the suggested enhanced algorithm is a superior alternative for optimization research.