IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v47y2016i2p474-491.html
   My bibliography  Save this article

Hybrid real-code ant colony optimisation for constrained mechanical design

Author

Listed:
  • Nantiwat Pholdee
  • Sujin Bureerat

Abstract

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

Suggested Citation

  • Nantiwat Pholdee & Sujin Bureerat, 2016. "Hybrid real-code ant colony optimisation for constrained mechanical design," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(2), pages 474-491, January.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:2:p:474-491
    DOI: 10.1080/00207721.2014.891664
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2014.891664
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2014.891664?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. Chih-Ming Hsu, 2012. "Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(5), pages 972-986.
    2. Cheng-Dar Liou & Yi-Chih Hsieh & Yin-Yann Chen, 2013. "A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(1), pages 77-93.
    3. Chelouah, Rachid & Siarry, Patrick, 2005. "A hybrid method combining continuous tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions," European Journal of Operational Research, Elsevier, vol. 161(3), pages 636-654, March.
    4. Yoel Tenne & Kazuhiro Izui & Shinji Nishiwaki, 2012. "A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(7), pages 1305-1321.
    5. Herrera, F. & Lozano, M. & Molina, D., 2006. "Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies," European Journal of Operational Research, Elsevier, vol. 169(2), pages 450-476, March.
    6. Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2011. "A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1901-1909.
    7. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    Full references (including those not matched with items on IDEAS)

    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. Gouvêa, Érica J.C. & Regis, Rommel G. & Soterroni, Aline C. & Scarabello, Marluce C. & Ramos, Fernando M., 2016. "Global optimization using q-gradients," European Journal of Operational Research, Elsevier, vol. 251(3), pages 727-738.
    2. Hvattum, Lars Magnus & Glover, Fred, 2009. "Finding local optima of high-dimensional functions using direct search methods," European Journal of Operational Research, Elsevier, vol. 195(1), pages 31-45, May.
    3. Weitao Sun & Yuan Dong, 2011. "Study of multiscale global optimization based on parameter space partition," Journal of Global Optimization, Springer, vol. 49(1), pages 149-172, January.
    4. Amjad Hudaib & Mohammad Khanafseh & Ola Surakhi, 2018. "An Improved Version of K-medoid Algorithm using CRO," Modern Applied Science, Canadian Center of Science and Education, vol. 12(2), pages 116-116, February.
    5. Liao, Tianjun & Stützle, Thomas & Montes de Oca, Marco A. & Dorigo, Marco, 2014. "A unified ant colony optimization algorithm for continuous optimization," European Journal of Operational Research, Elsevier, vol. 234(3), pages 597-609.
    6. Witanowski, Ł. & Klonowicz, P. & Lampart, P. & Suchocki, T. & Jędrzejewski, Ł. & Zaniewski, D. & Klimaszewski, P., 2020. "Optimization of an axial turbine for a small scale ORC waste heat recovery system," Energy, Elsevier, vol. 205(C).
    7. Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2011. "A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1901-1909.
    8. Ali Sardar Shahraki & Mohim Tash & Tommaso Caloiero & Ommolbanin Bazrafshan, 2024. "Optimal Allocation of Water Resources Using Agro-Economic Development and Colony Optimization Algorithm," Sustainability, MDPI, vol. 16(13), pages 1-18, July.
    9. Luo, Qifang & Yang, Xiao & Zhou, Yongquan, 2019. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 57-92.
    10. Oscar Cordón & Sergio Damas & Jose Santamaría & Rafael Martí, 2008. "Scatter Search for the Point-Matching Problem in 3D Image Registration," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 55-68, February.
    11. Shuaipeng Yuan & Tieke Li & Bailin Wang, 2021. "A discrete differential evolution algorithm for flow shop group scheduling problem with sequence-dependent setup and transportation times," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 427-439, February.
    12. Niknam, Taher & Firouzi, Bahman Bahmani, 2009. "A practical algorithm for distribution state estimation including renewable energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2309-2316.
    13. Qiang Yang & Xu Guo & Xu-Dong Gao & Dong-Dong Xu & Zhen-Yu Lu, 2022. "Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(8), pages 1-32, April.
    14. A Corominas & R Pastor, 2011. "Designing greedy algorithms for the flow-shop problem by means of Empirically Adjusted Greedy Heuristics (EAGH)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1704-1710, September.
    15. Hakan Yılmazer & Selma Ayşe Özel, 2024. "Diverse but Relevant Recommendations with Continuous Ant Colony Optimization," Mathematics, MDPI, vol. 12(16), pages 1-26, August.
    16. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    17. Gao, Wei-feng & Huang, Ling-ling & Liu, San-yang & Chan, Felix T.S. & Dai, Cai & Shan, Xian, 2015. "Artificial bee colony algorithm with multiple search strategies," Applied Mathematics and Computation, Elsevier, vol. 271(C), pages 269-287.
    18. Liou, Cheng-Dar & Hsieh, Yi-Chih, 2015. "A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 258-267.
    19. Ana Maria A. C. Rocha & M. Fernanda P. Costa & Edite M. G. P. Fernandes, 2017. "On a smoothed penalty-based algorithm for global optimization," Journal of Global Optimization, Springer, vol. 69(3), pages 561-585, November.
    20. Behzad Ataie-Ashtiani & Hamed Ketabchi, 2011. "Elitist Continuous Ant Colony Optimization Algorithm for Optimal Management of Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 165-190, January.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tsysxx:v:47:y:2016:i:2:p:474-491. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.