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

Honeybees-inspired heuristic algorithms for numerical optimisation