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Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions

Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions

Mohamed Amine Nemmich, Fatima Debbat, Mohamed Slimane
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 29
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802853|DOI: 10.4018/IJAMC.2020040102
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MLA

Nemmich, Mohamed Amine, et al. "Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions." IJAMC vol.11, no.2 2020: pp.27-55. http://doi.org/10.4018/IJAMC.2020040102

APA

Nemmich, M. A., Debbat, F., & Slimane, M. (2020). Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions. International Journal of Applied Metaheuristic Computing (IJAMC), 11(2), 27-55. http://doi.org/10.4018/IJAMC.2020040102

Chicago

Nemmich, Mohamed Amine, Fatima Debbat, and Mohamed Slimane. "Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.2: 27-55. http://doi.org/10.4018/IJAMC.2020040102

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Abstract

In this article, two hybrid schemes using the Bees Algorithm (BA) and the Firefly Algorithm (FA) are presented for numerical complex problem resolution. The BA is a recent population-based optimization algorithm, which tries to imitate the natural behaviour of honey bees foraging for food. The FA is a swarm intelligence technique based upon the communication behaviour and the idealized flashing features of tropical fireflies. The first approach, called the Hybrid Bee Firefly Algorithm (HBAFA), centres on improvements to the BA with FA during the local search thus increasing exploitation in each research zone. The second one, namely the Hybrid Firefly Bee Algorithm (HFBA), uses FA in the initialization step for a best exploration and detection of promising areas in research space. The performance of the novel hybrid algorithms was investigated on a set of various benchmarks and compared with standard BA, and other methods found in the literature. The results show that the proposed algorithms perform better than the Standard BA, and confirm their effectiveness in solving continuous optimization functions.

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