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Prairie Dog Optimization Algorithm

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Abstract

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two common optimization phases, exploration and exploitation. The prairie dogs' foraging and burrow build activities are used to provide exploratory behaviour for PDO. The prairie dogs build their burrows around an abundant food source. As the food source gets depleted, they search for a new food source and build new burrows around it, exploring the whole colony or problem space to discover new food sources or solutions. The specific response of the prairie dogs to two unique communication or alert sound is used to accomplish exploitation. The prairie dogs have signals or sounds for different scenarios ranging from predator threats to food availability. Their communication skills play a significant role in satisfying the prairie dogs' nutritional needs and anti-predation abilities. These two specific behaviours result in the prairie dogs converging to a specific location or a promising location in the case of PDO implementation, where further search (exploitation) is carried out to find better or near-optimal solutions. The performance of PDO in carrying out optimization is tested on a set of twenty-two classical benchmark functions and ten CEC 2020 test functions. The experimental results demonstrate that PDO benefits from a good balance of exploration and exploitation. Compared with the results of other well-known population-based metaheuristic algorithms available in the literature, the PDO shows stronger performance and higher capabilities than the other algorithms. Furthermore, twelve benchmark engineering design problems are used to test the performance of PDO, and the results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global optima. The PDO algorithm source codes is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/110980-prairie-dog-optimization-algorithm.

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Authors and Affiliations

  1. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa

    Absalom E. Ezugwu & Jeffrey O. Agushaka

  2. Department of Computer Science, Federal University of Lafia, LafiaNasarawa State, 950101, Nigeria

    Jeffrey O. Agushaka

  3. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  4. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  5. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

  6. Centre for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia

    Seyedali Mirjalili

  7. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  8. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Amir H. Gandomi

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  1. Absalom E. Ezugwu
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Ezugwu, A.E., Agushaka, J.O., Abualigah, L. et al. Prairie Dog Optimization Algorithm. Neural Comput & Applic 34, 20017–20065 (2022). https://doi.org/10.1007/s00521-022-07530-9

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