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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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.

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