Prairie Dog Optimization Algorithm

• Published in: Neural computing & applications Vol. 34; no. 22; pp. 20017 - 20065
• Main Authors: Ezugwu, Absalom E., Agushaka, Jeffrey O., Abualigah, Laith, Mirjalili, Seyedali, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2022; Springer Nature B.V
• Subjects: Acoustics; Algorithms; Artificial Intelligence; Availability; Benchmarks; Communication skills; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Design engineering; Exploitation; Food; Foraging habitats; Heuristic methods; Image Processing and Computer Vision; Optimization; Original Article; Prairie dogs; Probability and Statistics in Computer Science; Space colonies; Optimization algorithm; Prairie dogs; Anti-predation; Burrow-building; Swarm intelligence; Nature-inspired; Optimization

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 exploitatio...