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A Novel Hybrid Firefly Algorithm for Global Optimization

Author

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  • Lina Zhang
  • Liqiang Liu
  • Xin-She Yang
  • Yuntao Dai

Abstract

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

Suggested Citation

  • Lina Zhang & Liqiang Liu & Xin-She Yang & Yuntao Dai, 2016. "A Novel Hybrid Firefly Algorithm for Global Optimization," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-17, September.
    • Handle: RePEc:plo:pone00:0163230
    DOI: 10.1371/journal.pone.0163230
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    References listed on IDEAS

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    1. Du, Wen-Bo & Gao, Yang & Liu, Chen & Zheng, Zheng & Wang, Zhen, 2015. "Adequate is better: particle swarm optimization with limited-information," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 832-838.
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    2. B. Koti Reddy & Amit Kumar Singh, 2021. "Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods," Energies, MDPI, vol. 14(16), pages 1-28, August.
    3. Javaid Ali & Muhammad Saeed & Muhammad Farhan Tabassam & Shaukat Iqbal, 2019. "Controlled showering optimization algorithm: an intelligent tool for decision making in global optimization," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 132-164, June.
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    5. Dinesh Karunanidy & Subramanian Ramalingam & Ankur Dumka & Rajesh Singh & Mamoon Rashid & Anita Gehlot & Sultan S. Alshamrani & Ahmed Saeed AlGhamdi, 2022. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem," Mathematics, MDPI, vol. 10(5), pages 1-28, February.
    6. Umesh Balande & Deepti Shrimankar, 2020. "An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems," Operational Research, Springer, vol. 20(2), pages 985-1010, June.
    7. Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    8. Ivona Brajević & Predrag S. Stanimirović & Shuai Li & Xinwei Cao & Ameer Tamoor Khan & Lev A. Kazakovtsev, 2022. "Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems," Mathematics, MDPI, vol. 10(23), pages 1-21, December.
    9. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, November.

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