proposed a hybrid optimization method of the Beetle-Antenna Search Algorithm and Particle Swarm Optimization (PSO) [26]. The BAS has good opti  
Figure 1: The category of Metaheuristic Algorithms. mizing speed and accuracy in low-dimensional optimization problems, but it is easy to fall into local optimization in high-dimensional problems. Combining the PSO algorithm with the BAS can improve the BAS's optimization ability. Moreover, the BAS [27] can be applied to other Metaheuristic Algorithms to overcome the shortcomings of a single algorithm. For instance, [PERSON] et al. proposed a Flower-Pollination Algorithm (FPA) based on the Beetle-Antennae Search Algorithm to overcome the slow convergence problem of the original FPA algorithm [28]. The experimental results show that the improved optimization algorithm has a faster convergence rate. An improved Artificial-Bee-Colony Algorithm (ABC) [29] based on the Beetle-Antenna Search (BAS-ABC) was proposed by [PERSON] et al. in 2019 [30]. This algorithm makes use of the position-update ability of BAS to avoid the randomness of searching, so that the original algorithm can converge to the optimal solution more quickly. The Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS) is an effective swarm intelligence optimization algorithm. QIBAS has been applied to the Inverse Kinematics Solution Algorithm of Electric Climbing Robot Based on the Improved Beetle-Antennae Search [31]. It is universally believed that this improved algorithm improves the convergence accuracy. The teaching-Learning-Based Optimization (TLBO) [32] is a very mature swarm intelligence optimization algorithm, which has been used in the improvement and hybrid of many MAs. [PERSON] et al. proposed a hybrid algorithm based on Harmonious Search (HS) and Teaching-Learning-Based Optimization for complex high-dimensional problems [33]. HS has a strong global search capability, but its convergence speed is slow. TLBO can make up for this deficiency to increase the convergence rate. [PERSON] et al. used the TLBO algorithm to solve the job-shop-scheduling problem [34] and compared with other optimization algorithms. The experimental results show that TLBO algorithm is more efficient in the job-shop-scheduling problem. In order to solve the problem that the TLBO algorithm is prone to local convergence in complex problems, [PERSON] et al. designed the local learning and self-learning methods to improve the original TLBO algorithm [35]. It is proved that the improved TLBO has a better global search ability than other algorithms by testing on a few functions. Quasi-Reflection-Based Learning (QRBL) [36] is a variant of Opposition-Based Learning (OBL) [37], and it is an effective intelligent optimization technique. QRBL can be applied to Biogeography-Based Optimization (BBO) [38], Ion Motion Optimization (IMO) [39] and Symbiotic Organisms Search (SOS) [40]. The modified algorithm with QRBL has better convergence speed and the better ability to avoid the local optimal than the basic algorithm. The Gorilla Troops Optimizer (GTO) [41] is a new swarm intelligence optimization algorithm that was established by [PERSON] et al. in 2021. The inspiration of GTO is the migration, competition for adult females and following behavior of the gorilla colony. Currently, it has been applied to several subject-design and engineering-optimization problems. However, like other swarm intelligence optimization algorithms, GTO is difficult to obtain a balance between exploration and exploitation due to the randomness of the optimization process. Therefore, the algorithm still has some problems, such as low accuracy, slow convergence and ease of falling into local optimal. It is worth mentioning that the No Free Lunch (NFL) [42] theorem indicates that no algorithm can solve all optimization problems perfectly. Therefore, this theorem and the defects of GTO prompt us to improve and develop the modified swarm-based algorithm to deal with more engineering problems:  
1. A modified GTO, which is called MGTO, is proposed in this paper.