Many real-world problems are optimizable in nature. These problems often have numerous decision variables and constraints, making it difficult to optimize their objective function. To solve these problems, a group of algorithms known as metaheuristic algorithms has been introduced, which, considering the problem constraints, can find the optimal values of the decision variables in a reasonable amount of time to optimize the value of the objective function(s). Due to the importance and high application of these algorithms, a new metaheuristic optimization algorithm called Gray Langurs Optimizer (GLO) has been introduced in this research. The fundamental inspiration for the GLO is the group behavior of gray langurs in nature. The gray langurs are observed in three groups: one-male, multi-male, and all-male groups. The GLO mathematically simulates the group behavior and social hierarchy of gray langurs in all three groups. The migration of gray langurs between groups due to the death of the alpha or other group members, mate finding, and puberty is formulated by GLO. Additionally, the GLO models the arbitrary behavior of gray langurs within their territory. The GLO is tested on twenty-three classical test functions, including unimodal, multimodal, and fixed-dimension benchmarks, as well as twenty-seven CEC17 test functions, which comprise shifted, rotated, and composite benchmarks. The GLO is also applied to six real-world applications, and its results are compared with those of eight state-of-the-art algorithms both numerically and visually. The experimental results demonstrate the high capability of GLO in optimizing the various test functions and real-world applications. The MATLAB and Python implementations of GLO are available at https://nimakhodadadi.com.
Barshandeh et al. (Wed,) studied this question.