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A honeybees-inspired heuristic algorithm for numerical optimisation

Dugenci, Muharrem; Aydin, Mehmet Emin


Muharrem Dugenci

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Dr Mehmet Aydin
Senior Lecturer in Networks and Mobile Computing


© 2019, The Author(s). 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 contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.

Journal Article Type Article
Publication Date 2019
Journal Neural Computing and Applications
Print ISSN 0941-0643
Electronic ISSN 1433-3058
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
APA6 Citation Dugenci, M., & Aydin, M. E. (2019). A honeybees-inspired heuristic algorithm for numerical optimisation. Neural Computing and Applications,
Keywords Swarm intelligence; Numerical optimization; Bee-inspired algorithms; Diversification and intensification; Training Feedforward Neural Networks


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C The Author(s) 2019

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