White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO[sub.2] Emission Design of Retaining Structures

• Published in: Sustainability Vol. 14; no. 17
• Main Authors: Arandian, Behdad, Iraji, Amin, Alaei, Hossein, Keawsawasvong, Suraparb, Nehdi, Moncef L
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.08.2022
• Subjects: Air quality management; Algorithms; Carbon dioxide; Emissions (Pollution); Environmental aspects; Mathematical optimization; Methods; Germany; Iran
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su141710673

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 CO[sub.2] 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.