Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

• Published in: The Artificial intelligence review Vol. 57; no. 5; p. 123
• Main Authors: Fu, Youfa, Liu, Dan, Chen, Jiadui, He, Ling
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 23.04.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Birds; Computer Science; Convergence; Design engineering; Global optimization; Heuristic methods; Hunting; Optimization algorithms; Predator-prey simulation; Predators; Survival; Unmanned aerial vehicles; Secretary bird optimization algorithm; Exploration and exploitation; Engineering design problems; Heuristic algorithm; Nature-inspired optimization
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-024-10729-y

This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior of secretary birds in their natural environment. Survival for secretary birds involves continuous hunting for prey and evading pursuit from predators. This information is crucial for proposing a new metaheuristic algorithm that utilizes the survival abilities of secretary birds to address real-world optimization problems. The algorithm's exploration phase simulates secretary birds hunting snakes, while the exploitation phase models their escape from predators. During this phase, secretary birds observe the environment and choose the most suitable way to reach a secure refuge. These two phases are iteratively repeated, subject to termination criteria, to find the optimal solution to the optimization problem. To validate the performance of SBOA, experiments were conducted to assess convergence speed, convergence behavior, and other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using the CEC-2017 and CEC-2022 benchmark suites. All test results consistently demonstrated the outstanding performance of SBOA in terms of solution quality, convergence speed, and stability. Lastly, SBOA was employed to tackle 12 constrained engineering design problems and perform three-dimensional path planning for Unmanned Aerial Vehicles. The results demonstrate that, compared to contrasted optimizers, the proposed SBOA can find better solutions at a faster pace, showcasing its significant potential in addressing real-world optimization problems.