A novel Fibonacci-inspired enhancement of the Bees Algorithm: application to robotic disassembly sequence planning

Abstract

The Bees Algorithm (BA) is an intelligent, nature-inspired metaheuristic algorithm first introduced in 2005 and based on the foraging activity of honeybees. Enhancements to the BA have been made continually since its introduction, with some aimed at reducing the number of user-determined parameters. The goal is to achieve optimal results without extensive efforts required for parameter tuning. This article presents an enhanced version of the BA. The enhancement uses the Fibonacci sequence, which can be found in the family tree of the male bee (drone), to give the numbers of bees assigned to conduct local searches. The proposed BA is simple, having only four parameters that must be set by the user, as opposed to five in the basic combinatorial version. To evaluate the performance of the new algorithm in robotic disassembly sequence planning, a case study is conducted on gear pump disassembly, a commonly used benchmark problem in robotic disassembly. A comparison is made between the proposed Fibonacci Bees Algorithm and the Basic Bees Algorithm (BBA). The results are assessed using a statistical performance metric and a performance evaluation index. The findings demonstrate that the proposed algorithm is better than the BBA for more complex problems while exhibiting a similar performance on simpler tasks. Future research will explore broader applications for both continuous and combinatorial problems, demonstrating the versatility of the algorithm across different domains.

Keywords:

• Bees Algorithm
• disassembly
• Fibonacci
• optimisation
• remanufacturing

REVIEWING EDITOR:

• Zude Zhou, Wuhan University of Technology, China

SUBJECTS:

• Operation Research
• Optimization Techniques
• Computational Techniques
• Design of Experiments General

Acknowledgements

The authors would like to extend their sincere gratitude to Dr Jiayi Liu for generously providing the EDBA code for our research. The author would also like to thank the editor and the reviewers for their constructive feedback, which helped to improve the manuscript.

Disclosure statement

The authors declare no competing interests.

Additional information

Funding

This work was partly funded by the Engineering and Physical Sciences Research Council (EPSRC), UK, grant no. EP/N018524/1. Natalia Hartono’s doctoral study is supported by the Indonesian Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia, grant no. 201908222215155 under BUDI LN scheme.

Notes on contributors

Natalia Hartono

Natalia Hartono earned her doctoral degree in Mechanical Engineering from the University of Birmingham, UK, in 2023, specialising in intelligent optimisation, the bees algorithm, sustainability, robotic disassembly, and remanufacturing. She has made contributions to these fields, with publications in reputable journals, research poster and thesis presentation competitions, and presentations at international conferences. She is part of the Bees Algorithm Research Group and Autonomous Remanufacturing Group at the University of Birmingham. Dr Hartono is well-known as the co-chair of the International Workshop Series on the Bees Algorithm and Its Applications and the co-editor of the book “Intelligent Production and Manufacturing Optimisation – The Bees Algorithm Approach” published by Springer.