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A Novel Quantum Firefly Algorithm for Global Optimization

Arabian Journal for Science and Engineering volume 46, pages 8741–8759 (2021)Cite this article

Abstract

A novel nature-inspired metaheuristic optimization algorithm, called the quantum firefly algorithm, is proposed in this paper. The algorithm imitates (a) the social behaviour of fireflies mating in nature, (b) laws of quantum physics, and (c) laws of natural evolution. The algorithm combines the powers of two well-known algorithms: the firefly algorithm and the quantum genetic algorithm. The proposed quantum firefly algorithm’s performance is tested on 15 mathematical test functions and one structural design problem. The obtained results show that the quantum firefly algorithm is very competitive compared to the firefly algorithm and the quantum genetic algorithm.

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Notes

  1. www.benchmarkfcns.xyz/fcns.

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Acknowledgements

This research work is supported by UAEU Grant: 31T102-UPAR-1-2017.

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Authors and Affiliations

  1. Department of Computer Science, Kasdi Merbah University, Ouargla, Algeria

    Farouq Zitouni

  2. LIRE Laboratory, Abdelhamid Mehri University, Constantine, Algeria

    Farouq Zitouni & Ramdane Maamri

  3. Department of Computer Science and Software Engineering, UAE University - Abu Dhabi, Al Ain, United Arab Emirates

    Saad Harous

  4. Department of Computer Science, Abdelhamid Mehri University, Constantine, Algeria

    Ramdane Maamri

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  1. Farouq Zitouni
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Zitouni, F., Harous, S. & Maamri, R. A Novel Quantum Firefly Algorithm for Global Optimization. Arab J Sci Eng 46, 8741–8759 (2021). https://doi.org/10.1007/s13369-021-05608-5

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Keywords

  • Firefly algorithm
  • Quantum genetic algorithm
  • Global optimization
  • Metaheuristics
  • Test functions