Honeybees-inspired heuristic algorithms for numerical optimisation
This work addresses the challenge of solving hard numerical optimization problems, but it is incremental as it builds upon existing bee algorithms.
The authors tackled the problem of improving numerical optimization by proposing two revised versions and a hybrid of honeybee-inspired swarm intelligence algorithms, which outperformed the original algorithms on hard benchmark problems.
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.