Nature-inspired optimization algorithms (NIOAs) have attracted enormous attention thanks to their great capabilities in solving complex problems. This paper presents the novel Actiniaria optimization algorithm (ACTOA), inspired by the behavior and biological characteristics of Actiniaria (sea anemones). Actiniaria are known to have unique abilities to survive and interact with various marine environments. Therefore, they can provide an appropriate model for designing an optimization algorithm. This study aimed to balance the exploration and exploitation phases using Actiniaria’s two biological mechanisms: hunting and spawning. The exploration phase is developed with a hunting mechanism as a normal distribution of the searching particles with a reduced standard deviation (SD) around the best searching particle. Next, the dispersal of Actiniaria’s eggs in the exploitation phase under forces such as wind and ocean waves is simulated. The performance of ACTOA is assessed using a set of optimization parameters. The advantages of the algorithm’s performance were also examined by 59 test functions, and ACTOA outperformed modern algorithms. Ultimately, optimization of the three dams of Sariyar, Shafaroud, and Pine Flat was put on the agenda and the proposed algorithm showed that optimal solutions were found by the 700th, 840th, and 985th iterations, which resulted in savings of 28.2, 30, and 3.5 percent in concrete volume, respectively.
Faraji et al. (Wed,) studied this question.