Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems

• Published in: IEEE access Vol. 9; pp. 162059 - 162080
• Main Authors: Dehghani, Mohammad, Hubalovsky, Stepan, Trojovsky, Pavel
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Design engineering; Exploitation; exploration; Games; Genetic algorithms; Hunting; Linear programming; Machine learning; Mathematical models; northern goshawk; Optimization; optimization problem; Particle swarm optimization; Predator-prey simulation; Search algorithms; Search problems; Sociology; Statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3133286

Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strategy includes two phases of prey identification and the tail and chase process. The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. The ability of NGO to solve optimization problems is evaluated on sixty-eight different objective functions. To analyze the quality of the results, the proposed NGO algorithm is compared with eight well-known algorithms, particle swarm optimization, genetic algorithm, teaching-learning based optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine predators algorithm. In addition, for further analysis, the proposed algorithm is also employed to solve four engineering design problems. The results of simulations and experiments show that the proposed NGO algorithm, by creating a proper balance between exploration and exploitation, has an effective performance in solving optimization problems and is much more competitive than similar algorithms.