Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems.Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.
K E Y W O R D Sgorilla troops optimizer, metaheuristic algorithms, optimization Water Evaporation Optimization (WEO) algorithm, 41 Glowworm Swarm Optimization (GSO), 42 Dolphin echolocation optimization (DEO), 43 and Water Cycle Algorithm (WCA). 44 Generally, the No Free Lunch (NFL) Theorems 45,46 states that, on average, all nonsampling optimization algorithms perform equally well in solving almost all optimization problems. 47 This hypothesis also states that all black box search algorithms and optimization algorithms have the same function in all possible target functions in a fixed search space. On the other hand, however, there is no algorithm to solve all real-world problems accurately and well. 48 For this reason, in NLF theory, an algorithm has been aligned with the problem. NLF has also introduced a scenario where an existing algorithm can even be better than a random search. Problem subset knowledge of a random search can also often be precious; one of the most important reasons is simple execution and good performance. This is an important principle where the NFL does not apply. However, when and why can researchers ignore the NFL? That is unlikely.It appears that the researcher intends to make a specific claim about an algorithm that performs moderately compared with a set of possible problems in space, that is, (original NFL), a CUP set (SNFL), focus set (FNFL), or restricted set (RMNFL). However, it is clear that under such a situation, one cannot ignore NFL, and it is impossible to see any improvements in the random search. This is why a researcher may create a super algorithm better than a random one in all real-world problems. This could yield promising results, though NFL results cannot prevent this as researchers can also ignore NFL. In the end, a researcher hoping to have a super algorithm to be better than a random one in solving all problems may be neutralized by an NFL. 49 3 | GORILLA TROOPS Gorillas, like other apes, have feelings, make and use tools, establish strong family bonds, and think about their past and future. 50,51 Some researcher...
