Abstract
Metaheuristic algorithms have been widely used in science and engineering due to their ability to solve complex optimization problems, but striking an effective balance between exploration and exploitation remains one of the most important challenges in this field.In this study, Binturong Optimization Algorithm (BOA) is introduced as a new population-based metaheuristic algorithm inspired by the natural behaviors of Binturong.First, the biological behaviors of this animal are analyzed, and then BOA is modeled in three stages including initialization, exploration phase, and exploitation phase.exploration phase is designed based on Canopy Exploration behavior to extensively explore the search space and maintain population diversity, while exploitation phase is developed inspired by Scent-Guided Precision Foraging behavior to perform precise local search and improve promising solutions.The performance of BOA is evaluated using a comprehensive set of simulation studies.First, the search behavior of the algorithm is analyzed from the perspective of population diversity and exploration-exploitation dynamics.The results show that BOA strikes a good balance between exploration and exploitation during the optimization process and provides a stable and efficient search process.Then, the performance of the algorithm on unimodal test functions (F1-F7), high-dimensional multimodal test functions (F8-F13) and fixed-dimensional multimodal test functions (F14-F23) is investigated and compared with 9 well-known metaheuristic algorithms.The simulation results show that BOA ranks first in all 23 benchmark functions and, as a result, has achieved a 100% success rate compared to competing algorithms.These findings indicate that BOA, with its high ability to strike a balance between exploration and exploitation, is a powerful, stable and competitive optimizer and can be used in a wide range of optimization problems, various sciences and real-world applications.