Honeybees-inspired heuristic algorithms for numerical optimisation

TL;DR

This paper reviews honeybee-inspired swarm intelligence algorithms and introduces revisions and a hybrid version that outperform original algorithms on challenging numerical optimization benchmarks.

Contribution

It presents new revisions and a hybrid algorithm inspired by honeybees, enhancing efficiency in solving complex numerical optimization problems.

Findings

Hybrid algorithm outperforms original bee algorithms on benchmarks

Revisions improve the balance of diversification and intensification

Proposed methods effectively solve hard numerical optimization problems

Abstract

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.