White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO2 Emission Design of Retaining Structures

• Published in: Sustainability
• Main Authors: Behdad Arandian, Amin Iraji, Hossein Alaei, Suraparb Keawsawasvong, Moncef L. Nehdi
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: nature-inspired; white-tailed eagle; retaining structure; cost; CO 2 emissions; archi; manag; White-tailed eagle
• Online Access: https://doi.org/10.3390/su141710673; https://doaj.org/article/747f5a4c9ee74dd98ed2858fb884be38

This study proposes a new metaheuristic optimization algorithm, namely the white-tailed eagle algorithm (WEA), for global optimization and optimum design of retaining structures. Metaheuristic optimization methods are now broadly implemented to address problems in a variety of scientific domains. These algorithms are typically inspired by the natural behavior of an agent, which can be humans, animals, plants, or any physical agent. However, a specific metaheuristic algorithm (MA) may not be able to find the optimal solution for every situation. As a result, researchers will aim to propose and discover new methods in order to identify the best solutions to a variety of problems. The white-tailed eagle algorithm (WEA) is a simple but effective nature-inspired algorithm inspired by the social life and hunting activity of white-tailed eagles. The WEA’s hunting is divided into two phases. In the first phase (exploration), white-tailed eagles seek prey inside the searching region. The eagle goes inside the designated space according to the position of the best eagle to find the optimum hunting position (exploitation). The proposed approach is tested using 13 unimodal and multimodal benchmark test functions, and the results are compared to those obtained by some well-established optimization methods. In addition, the new algorithm automates the optimum design of retaining structures under seismic load, considering two objectives: economic cost and CO2 emissions. The results of the experiments and comparisons reveal that the WEA is a high-performance algorithm that can effectively explore the decision space and outperform almost all comparative algorithms in the majority of the problems.