IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i23p3111-d693796.html
   My bibliography  Save this article

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

Author

Listed:
  • Deeam Najmadeen Hama Rashid

    (Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil 44001, Iraq)

  • Tarik A. Rashid

    (Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil 44001, Iraq)

  • Seyedali Mirjalili

    (Centre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane, QLD 4006, Australia
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

Abstract

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.

Suggested Citation

  • Deeam Najmadeen Hama Rashid & Tarik A. Rashid & Seyedali Mirjalili, 2021. "ANA: Ant Nesting Algorithm for Optimizing Real-World Problems," Mathematics, MDPI, vol. 9(23), pages 1-30, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3111-:d:693796
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/23/3111/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/23/3111/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luca Maria Gambardella & Marco Dorigo, 2000. "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 237-255, August.
    2. Dale F. Cooper, 1976. "Heuristics for Scheduling Resource-Constrained Projects: An Experimental Investigation," Management Science, INFORMS, vol. 22(11), pages 1186-1194, July.
    3. Weyland, Dennis, 2015. "A critical analysis of the harmony search algorithm—How not to solve sudoku," Operations Research Perspectives, Elsevier, vol. 2(C), pages 97-105.
    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. Rainer Kolisch & Andreas Drexl, 1996. "Adaptive search for solving hard project scheduling problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(1), pages 23-40, February.
    2. Jan Böttcher & Andreas Drexl & Rainer Kolisch & Frank Salewski, 1999. "Project Scheduling Under Partially Renewable Resource Constraints," Management Science, INFORMS, vol. 45(4), pages 543-559, April.
    3. Drexl, Andreas & Kolisch, Rainer, 1991. "Produktionsplanung und -steuerung bei Einzel- und Kleinserienfertigung," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 281, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    4. 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.
    5. Vo[ss], Stefan & Witt, Andreas, 2007. "Hybrid flow shop scheduling as a multi-mode multi-project scheduling problem with batching requirements: A real-world application," International Journal of Production Economics, Elsevier, vol. 105(2), pages 445-458, February.
    6. Schirmer, Andreas & Riesenberg, Sven, 1997. "Parameterized heuristics for project scheduling: Biased random sampling methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 456, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    7. Schirmer, Andreas, 1995. "A guide to complexity theory in operations research," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 381, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    8. Aristide Mingozzi & Vittorio Maniezzo & Salvatore Ricciardelli & Lucio Bianco, 1998. "An Exact Algorithm for the Resource-Constrained Project Scheduling Problem Based on a New Mathematical Formulation," Management Science, INFORMS, vol. 44(5), pages 714-729, May.
    9. Fleming, Christopher L. & Griffis, Stanley E. & Bell, John E., 2013. "The effects of triangle inequality on the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 1-7.
    10. Kolisch, Rainer, 1994. "Serial and parallel resource-constrained projekt scheduling methodes revisited: Theory and computation," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 344, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    11. Hongbo Li & Linwen Zheng & Hanyu Zhu, 2023. "Resource leveling in projects with flexible structures," Annals of Operations Research, Springer, vol. 321(1), pages 311-342, February.
    12. Jing Yu & Lining Xing & Xu Tan, 0. "The new treatment mode research of hepatitis B based on ant colony algorithm," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-20.
    13. Nikolakopoulos, Athanassios & Sarimveis, Haralambos, 2007. "A threshold accepting heuristic with intense local search for the solution of special instances of the traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1911-1929, March.
    14. Hans, Erwin & Wullink, Gerhard & van Houdenhoven, Mark & Kazemier, Geert, 2008. "Robust surgery loading," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1038-1050, March.
    15. Böttcher, Jan & Drexl, Andreas & Kolisch, Rainer & Salewski, Frank, 1996. "Project scheduling under partially renewable resource constraints," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 398, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    16. V. Van Peteghem & M. Vanhoucke, 2009. "Using Resource Scarceness Characteristics to Solve the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/595, Ghent University, Faculty of Economics and Business Administration.
    17. Gambardella, L.M. & Montemanni, R. & Weyland, D., 2012. "Coupling ant colony systems with strong local searches," European Journal of Operational Research, Elsevier, vol. 220(3), pages 831-843.
    18. Kolisch, Rainer, 1994. "Efficient priority rules for the resource-constrained project scheduling problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 350, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    19. Lova, Antonio & Maroto, Concepcion & Tormos, Pilar, 2000. "A multicriteria heuristic method to improve resource allocation in multiproject scheduling," European Journal of Operational Research, Elsevier, vol. 127(2), pages 408-424, December.
    20. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.

    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:gam:jmathe:v:9:y:2021:i:23:p:3111-:d:693796. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.