The remanufacturing sector is vital to the circular economy and to ensuring sustainability. Optimising decision-making processes for EoL products is possible using the Bees Algorithm (BA) in digital twins. BA has proven effectiveness in addressing complex problems by drawing inspiration from the foraging behaviour of honeybees. To streamline parameter tuning, researchers have developed two versions of BA, Two-parameter BA (BA2) and Fibonacci BA (BAF). The objective of this study is to evaluate various versions of BA to determine the most efficient option for optimising remanufacturing decisions in product digital twins, based on prior work. The study evaluates the efficacy of the original BA, BA2, and BAF utilising prior data for the decision-making module in digital twins. The results indicate that there is a trade-off between the time required to adjust the parameters and the effectiveness of the different versions of the BA.