Enhanced redundancy allocation in biomedical manufacturing using opposition-based grey wolf optimiser

In the current environment of epidemics like COVID-19, biomedical gadgets are crucial. The production of biomedical devices greatly contributes to the community's ability to combat these diseases. This study aims to optimise redundancy in biomedical device production facilities due to globalisation and sophisticated machinery. Reliability optimisation is crucial for complex systems, and the redundancy allocation problem (RAP) is a significant optimisation problem in reliability engineering. Scholars are increasingly interested in RAP due to its significance in reliability and system engineering. This study compares grey wolf optimiser and opposition-based learning in high-tech sectors. Grey wolf optimiser enhances exploratory behaviour while maintaining a rapid convergence rate. The modified version of grey wolf optimiser solves the redundancy allocation problem, comparing outcomes with modified particle swarm optimisation. The study highlights the importance of utilising metaheuristic algorithms for optimisation.