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A review of chaos-based firefly algorithms: Perspectives and research challenges

Author

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  • Fister, Iztok
  • Perc, Matjaž
  • Kamal, Salahuddin M.
  • Fister, Iztok

Abstract

The firefly algorithm is a member of the swarm intelligence family of algorithms, which have recently showed impressive performances in solving optimization problems. The firefly algorithm, in particular, is applied for solving continuous and discrete optimization problems. In order to tackle different optimization problems efficiently and fast, many variants of the firefly algorithm have recently been developed. Very promising firefly versions use also chaotic maps in order to improve the randomness when generating new solutions and thereby increasing the diversity of the population. The aim of this review is to present a concise but comprehensive overview of firefly algorithms that are enhanced with chaotic maps, to describe in detail the advantages and pitfalls of the many different chaotic maps, as well as to outline promising avenues and open problems for future research.

Suggested Citation

  • Fister, Iztok & Perc, Matjaž & Kamal, Salahuddin M. & Fister, Iztok, 2015. "A review of chaos-based firefly algorithms: Perspectives and research challenges," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 155-165.
    • Handle: RePEc:eee:apmaco:v:252:y:2015:i:c:p:155-165
    DOI: 10.1016/j.amc.2014.12.006
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    References listed on IDEAS

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    1. Akemi Gálvez & Andrés Iglesias, 2013. "Firefly Algorithm for Polynomial Bézier Surface Parameterization," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-9, September.
    2. Xin-She Yang, 2011. "Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 1-11, October.
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    Cited by:

    1. 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.
    2. Jun, Luo & Liheng, Liu & Xianyi, Wu, 2015. "A double-subpopulation variant of the bat algorithm," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 361-377.
    3. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    4. Yu, Caiyang & Cai, Zhennao & Ye, Xiaojia & Wang, Mingjing & Zhao, Xuehua & Liang, Guoxi & Chen, Huiling & Li, Chengye, 2020. "Quantum-like mutation-induced dragonfly-inspired optimization approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 259-289.
    5. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    6. Fister, Iztok & Perc, Matjaž & Ljubič, Karin & Kamal, Salahuddin M. & Iglesias, Andres & Fister, Iztok, 2015. "Particle swarm optimization for automatic creation of complex graphic characters," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 29-35.
    7. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    8. Sujata Dash & Ajith Abraham & Ashish Kr Luhach & Jolanta Mizera-Pietraszko & Joel JPC Rodrigues, 2020. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.
    9. Wei, Zhouchao & Zhu, Bin & Yang, Jing & Perc, Matjaž & Slavinec, Mitja, 2019. "Bifurcation analysis of two disc dynamos with viscous friction and multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 265-281.
    10. Kisi, Ozgur & Shiri, Jalal & Karimi, Sepideh & Shamshirband, Shahaboddin & Motamedi, Shervin & Petković, Dalibor & Hashim, Roslan, 2015. "A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 731-743.
    11. Kostić, Srđan & Vasović, Nebojša & Sunarić, Duško, 2015. "A new approach to grid search method in slope stability analysis using Box–Behnken statistical design," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 425-437.
    12. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    13. Arvinder Kaur & Saibal K. Pal & Amrit Pal Singh, 2018. "New chaotic flower pollination algorithm for unconstrained non-linear optimization functions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 853-865, August.
    14. Panahi, Shirin & Aram, Zainab & Jafari, Sajad & Ma, Jun & Sprott, J.C., 2017. "Modeling of epilepsy based on chaotic artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 150-156.
    15. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.

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