Inspired by the foraging behaviour of bees in nature, the bees algorithmBees algorithm (BA) is a biomimetic optimisationOptimisation method designed according to how bees work together in choosing food sources and collecting high-quality honey. However, there are only a few mathematical studies on the convergence and optimisation problemOptimisation problem of BA in the literature, and most of the BA implementations are dependent on trial-and-error. Thus, this study attempts to use Markov chain theoryMarkov chain theory to enhance the understanding and analysis of BA convergence from the perspectives of the neighbourhood contraction strategy and site abandonment strategy. To improve the optimisationOptimisation performance of the BA, this study established a combination model of the BA and a dynamic particle swarm optimisation algorithmDynamic particle swarm optimisation algorithm on the local search process of the BA and the effect of the task allocation of scout beesScout bees for target location. Finally, the simulationSimulation experiment was conducted in MATLAB using the combination model. The simulationSimulation result shows that the probability of the combination model falling into the local optimum is much lower than that of the conventional BA based on the good convergence performanceConvergence performance for the optimisation problemOptimisation problem. It can enrich the theoretical base of the BA as a resource for convergence performanceConvergence performance and further optimisationOptimisation analysis.