IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v234y2014i3p597-609.html
   My bibliography  Save this article

A unified ant colony optimization algorithm for continuous optimization

Author

Listed:
  • Liao, Tianjun
  • Stützle, Thomas
  • Montes de Oca, Marco A.
  • Dorigo, Marco

Abstract

In this article, we propose UACOR, a unified ant colony optimization (ACO) algorithm for continuous optimization. UACOR includes algorithmic components from ACOR,DACOR and IACOR-LS, three ACO algorithms for continuous optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms; in addition, from UACOR we can also generate new continuous ACO algorithms that have not been considered before in the literature. In fact, UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new ACO algorithms. To show the benefits of UACOR’s flexibility, we automatically configure two new ACO algorithms, UACOR-s and UACOR-c, and evaluate them on two sets of benchmark functions from a recent special issue of the Soft Computing (SOCO) journal and the IEEE 2005 Congress on Evolutionary Computation (CEC’05), respectively. We show that UACOR-s is competitive with the best of the 19 algorithms benchmarked on the SOCO benchmark set and that UACOR-c performs superior to IPOP-CMA-ES and statistically significantly better than five other algorithms benchmarked on the CEC’05 set. These results show the high potential ACO algorithms have for continuous optimization and suggest that automatic algorithm configuration is a viable approach for designing state-of-the-art continuous optimizers.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:234:y:2014:i:3:p:597-609
    DOI: 10.1016/j.ejor.2013.10.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713008473
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.10.024?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. 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)

    Citations

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


    Cited by:

    1. Zhaojun Zhang & Zhaoxiong Xu & Shengyang Luan & Xuanyu Li & Yifei Sun, 2020. "Opposition-Based Ant Colony Optimization Algorithm for the Traveling Salesman Problem," Mathematics, MDPI, vol. 8(10), pages 1-16, September.
    2. Anand Kumar & Manoj Thakur & Garima Mittal, 2018. "A new ants interaction scheme for continuous optimization problems," 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 784-801, August.
    3. Stefano Bromuri, 2019. "Dynamic heuristic acceleration of linearly approximated SARSA( $$\lambda $$ λ ): using ant colony optimization to learn heuristics dynamically," Journal of Heuristics, Springer, vol. 25(6), pages 901-932, December.
    4. Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
    5. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.
    6. Fayez Alanazi & Ibrahim Khalil Umar & Sadi Ibrahim Haruna & Mahmoud El-Kady & Abdelhalim Azam, 2023. "Development of Artificial Intelligence Based Safety Performance Measures for Urban Roundabouts," Sustainability, MDPI, vol. 15(14), pages 1-17, July.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Hakan Yılmazer & Selma Ayşe Özel, 2024. "Diverse but Relevant Recommendations with Continuous Ant Colony Optimization," Mathematics, MDPI, vol. 12(16), pages 1-26, August.
    6. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    7. 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.
    8. 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.
    9. 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.
    10. Md. Hossain & A. El-shafie, 2013. "Intelligent Systems in Optimizing Reservoir Operation Policy: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3387-3407, July.
    11. Warren Liao, T. & Chang, P.C., 2010. "Impacts of forecast, inventory policy, and lead time on supply chain inventory--A numerical study," International Journal of Production Economics, Elsevier, vol. 128(2), pages 527-537, December.
    12. Broderick Crawford & Ricardo Soto & Gino Astorga & José García & Carlos Castro & Fernando Paredes, 2017. "Putting Continuous Metaheuristics to Work in Binary Search Spaces," Complexity, Hindawi, vol. 2017, pages 1-19, May.
    13. Asghar Mahdavi & Mohammad Shiri, 2015. "An augmented Lagrangian ant colony based method for constrained optimization," Computational Optimization and Applications, Springer, vol. 60(1), pages 263-276, January.
    14. Stefano Bromuri, 2019. "Dynamic heuristic acceleration of linearly approximated SARSA( $$\lambda $$ λ ): using ant colony optimization to learn heuristics dynamically," Journal of Heuristics, Springer, vol. 25(6), pages 901-932, December.
    15. Ahmad Wedyan & Jacqueline Whalley & Ajit Narayanan, 2017. "Hydrological Cycle Algorithm for Continuous Optimization Problems," Journal of Optimization, Hindawi, vol. 2017, pages 1-25, December.
    16. Peter Korošec & Jurij Šilc, 2013. "The continuous differential ant-stigmergy algorithm for numerical optimization," Computational Optimization and Applications, Springer, vol. 56(2), pages 481-502, October.
    17. Khaled Loukhaoukha, 2013. "Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain," Journal of Optimization, Hindawi, vol. 2013, pages 1-10, June.
    18. Li, Haibao & Cai, Zhiqiang & Zhang, Shuai & Zhao, Jiangbin & Si, Shubin, 2024. "Time series importance measure-based reliability optimization for cellular manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    19. Bera, Sasadhar & Mukherjee, Indrajit, 2016. "A multistage and multiple response optimization approach for serial manufacturing system," European Journal of Operational Research, Elsevier, vol. 248(2), pages 444-452.
    20. Zhang, Zhe & Song, Xiaoling & Gong, Xue & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects," European Journal of Operational Research, Elsevier, vol. 313(2), pages 452-464.

    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:ejores:v:234:y:2014:i:3:p:597-609. 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.elsevier.com/locate/eor .

    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.