IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v252y2015icp155-165.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300314016518
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2014.12.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Ninness, Brett, 2024. "Design of fractional-order hammerstein control auto-regressive model for heat exchanger system identification: Treatise on fuzzy-evolutionary computing," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Abubaker Younis & Fatima Belabbes & Petru Adrian Cotfas & Daniel Tudor Cotfas, 2024. "Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model," Forecasting, MDPI, vol. 6(2), pages 1-21, May.
    16. 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.
    17. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    2. 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.
    3. Hossein Moayedi & Amir Mosavi, 2021. "Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings," Energies, MDPI, vol. 14(6), pages 1-19, March.
    4. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:252:y:2015:i:c:p:155-165. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.