IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v14y2015i06ns021962201450031x.html
   My bibliography  Save this article

Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems

Author

Listed:
  • Surafel Luleseged Tilahun

    (Computational Science Program, Faculty of Science, Addis Ababa University, 1176, Addis Ababa, Ethiopia)

  • Hong Choon Ong

    (School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia)

Abstract

Nature-inspired optimization algorithms have become useful in solving difficult optimization problems in different disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be expressed numerically and is called the survival value. A prey will run towards the pack of preys with better surviving values and away from the predator. The predator chases the prey with the smallest survival value. However, the best prey or the prey with the best survival value performs a local search. Hence the best prey focuses fully on exploitation while the other solution members focus on the exploration of the solution space. The algorithm is tested on selected well-known test problems and a comparison is also done between our algorithm, genetic algorithm and particle swarm optimization. From the simulation result, it is shown that on the selected test problems prey-predator algorithm performs better in achieving the optimal value.

Suggested Citation

  • Surafel Luleseged Tilahun & Hong Choon Ong, 2015. "Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1331-1352, November.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:06:n:s021962201450031x
    DOI: 10.1142/S021962201450031X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S021962201450031X
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S021962201450031X?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. Hsin-Yun Lee, 2012. "A Decision Support System For Exposition Timetabling Using Ant Colony Optimization," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(03), pages 609-626.
    2. Surafel Luleseged Tilahun & Hong Choon Ong, 2013. "Vector optimisation using fuzzy preference in evolutionary strategy based firefly algorithm," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 16(1), pages 81-95.
    3. Lanshun Nie & Xiaofei Xu & Dechen Zhan, 2008. "Collaborative Planning In Supply Chains By Lagrangian Relaxation And Genetic Algorithms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 183-197.
    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. Nawaf N. Hamadneh & Muhammad Tahir & Waqar A. Khan, 2021. "Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico," Mathematics, MDPI, vol. 9(2), pages 1-14, January.
    2. 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.
    3. Waseem S Khan & Nawaf N Hamadneh & Waqar A Khan, 2017. "Prediction of thermal conductivity of polyvinylpyrrolidone (PVP) electrospun nanocomposite fibers using artificial neural network and prey-predator algorithm," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-17, 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. Kun Guo & Qishan Zhang, 2017. "A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1339-1357, September.
    2. Guodong Yu & Li Zhang & Huiping Sun, 2018. "A Method for Partner Selection of Supply Chain Using Interval-Valued Fuzzy Sets — Fuzzy Choquet Integral and Improved Dempster–Shafer Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1777-1804, November.
    3. Gregorio Rius-Sorolla & Julien Maheut & Sofia Estelles-Miguel & Jose P. Garcia-Sabater, 2021. "Collaborative Distributed Planning with Asymmetric Information. A Technological Driver for Sustainable Development," Sustainability, MDPI, vol. 13(12), pages 1-23, June.
    4. G. Rius-Sorolla & J. Maheut & S. Estellés-Miguel & J. P. Garcia-Sabater, 2020. "Coordination mechanisms with mathematical programming models for decentralized decision-making: a literature review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 61-104, March.

    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:wsi:ijitdm:v:14:y:2015:i:06:n:s021962201450031x. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    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.