IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v192y2022icp514-536.html
   My bibliography  Save this article

Chaotic hunger games search optimization algorithm for global optimization and engineering problems

Author

Listed:
  • Kutlu Onay, Funda
  • Aydemı̇r, Salih Berkan

Abstract

Chaotic maps have the characteristics of ergodicity and non-repeatability. Owing to these properties, they provide fast convergence by effectively scanning the search space in a metaheuristic optimization algorithm. The Hunger Games Search (HGS) is a metaheuristic algorithm modeled on the foraging and hunger instincts of animals. In this study, ten chaotic maps have been applied to the classical HGS method. The control of two random values in the HGS algorithm has been carried out with chaotic maps in three alternative scenarios. Accordingly, it has been observed that Scenario 2 exhibits a more stable and faster convergence than other scenarios. The performance of the proposed chaotic HGS has been evaluated on CEC2017 and 23 classical benchmark problems. The proposed algorithm has been applied to real engineering problems for cantilever beam design, tension/compression, and speed reducer, and the results have been compared with classical HGS and state-of-art algorithms in the literature. It can be seen that chaotic HGS yields promising results compared to other studies in the literature.

Suggested Citation

  • Kutlu Onay, Funda & Aydemı̇r, Salih Berkan, 2022. "Chaotic hunger games search optimization algorithm for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 514-536.
  • Handle: RePEc:eee:matcom:v:192:y:2022:i:c:p:514-536
    DOI: 10.1016/j.matcom.2021.09.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475421003426
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2021.09.014?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. Fister, Iztok & Iglesias, Andres & Galvez, Akemi & Del Ser, Javier & Osaba, Eneko & Fister, Iztok & Perc, Matjaž & Slavinec, Mitja, 2019. "Novelty search for global optimization," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 865-881.
    2. Harish Sharma & Jagdish Chand Bansal & K. V. Arya & Xin-She Yang, 2016. "Lévy flight artificial bee colony algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(11), pages 2652-2670, August.
    3. Du, Wenbo & Zhang, Mingyuan & Ying, Wen & Perc, Matjaž & Tang, Ke & Cao, Xianbin & Wu, Dapeng, 2018. "The networked evolutionary algorithm: A network science perspective," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 33-43.
    4. Yuan, Xiaohui & Yuan, Yanbin & Zhang, Yongchuan, 2002. "A hybrid chaotic genetic algorithm for short-term hydro system scheduling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(4), pages 319-327.
    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. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    2. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    3. Tsu-Yang Wu & Ankang Shao & Jeng-Shyang Pan, 2023. "CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm," Mathematics, MDPI, vol. 11(10), pages 1-43, May.

    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. 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.
    2. Zhang, Huifeng & Yue, Dong & Xie, Xiangpeng & Dou, Chunxia & Sun, Feng, 2017. "Gradient decent based multi-objective cultural differential evolution for short-term hydrothermal optimal scheduling of economic emission with integrating wind power and photovoltaic power," Energy, Elsevier, vol. 122(C), pages 748-766.
    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. Nidhi Rehani & Ritu Garg, 2018. "Meta-heuristic based reliable and green workflow scheduling in cloud computing," 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 811-820, August.
    5. Assif Assad & Kusum Deep, 2018. "Harmony search based memetic algorithms for solving sudoku," 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 741-754, August.
    6. Hamdy M. Ahmed & Mahmoud M. El-Borai & Hassan M. El-Owaidy & Ahmed S. Ghanem, 2019. "Existence Solution and Controllability of Sobolev Type Delay Nonlinear Fractional Integro-Differential System," Mathematics, MDPI, vol. 7(1), pages 1-14, January.
    7. Qian, Qian & Chao, Xiangrui & Feng, Hairong, 2023. "Internal or external control? How to respond to credit risk contagion in complex enterprises network," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. Wang, Jianwei & Xu, Wenshu & Chen, Wei & Yu, Fengyuan & He, Jialu, 2021. "Information sharing can suppress the spread of epidemics: Voluntary vaccination game on two-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    9. Jebaraj, Luke & Venkatesan, Chakkaravarthy & Soubache, Irisappane & Rajan, Charles Christober Asir, 2017. "Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1206-1220.
    10. Fang-Fang Li & Jia-Hua Wei & Xu-Dong Fu & Xin-Yu Wan, 2012. "An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4073-4090, November.
    11. V. Jothiprakash & R. Arunkumar, 2013. "Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1963-1979, May.
    12. Cui, Yibing & Hu, Wei & Rahmani, Ahmed, 2023. "Fractional-order artificial bee colony algorithm with application in robot path planning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 47-64.
    13. Li, Xiaosi & Li, Jiayi & Yang, Haichuan & Wang, Yirui & Gao, Shangce, 2022. "Population interaction network in representative differential evolution algorithms: Power-law outperforms Poisson distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    14. Gheisariha, Elmira & Tavana, Madjid & Jolai, Fariborz & Rabiee, Meysam, 2021. "A simulation–optimization model for solving flexible flow shop scheduling problems with rework and transportation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 180(C), pages 152-178.
    15. Zhao, Li & Li, Yuqi & Li, Shuai & Ke, Hanchen, 2023. "A frequency item mining based embedded feature selection algorithm and its application in energy consumption prediction of electric bus," Energy, Elsevier, vol. 271(C).
    16. Yang, Han-Xin & Sun, Lei, 2020. "Heterogeneous donation game in geographical small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    17. Wang, Chunyu & Zhang, Fan & Deng, Yue & Gao, Chao & Li, Xianghua & Wang, Zhen, 2020. "An adaptive population control framework for ACO-based community detection," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    18. Alizadeh, Somayeh & Ghazanfari, Mehdi, 2009. "Learning FCM by chaotic simulated annealing," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1182-1190.
    19. Yan, Zheping & Yan, Jinyu & Wu, Yifan & Cai, Sijia & Wang, Hongxing, 2023. "A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 55-86.
    20. R. Arunkumar & V. Jothiprakash, 2013. "Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5207-5222, December.

    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:matcom:v:192:y:2022:i:c:p:514-536. 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: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.