Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remains as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this study, two swarm intelligence algorithms inspired of natural honeybee colonies have been overviewed with many respects and two new revisions and a hybrid version have been studied to improve the efficiencies in solving numerical optimisation problems, which are well-known hard benchmarks. Consequently, the revisions and especially the hybrid algorithm proposed have outperformed the two original bee algorithms in solving these very hard numerical optimisation benchmarks.