Nail Technician Optimization Algorithm: A Novel Human-inspired Metaheuristic for Solving Optimization Applications

In this paper, a new metaheuristic algorithm based on human interactions, called Nail Technician Optimization Algorithm (NTOA), is introduced, which is inspired by the interaction process between a nail technician and customers.First, the theoretical foundations of the NTOA algorithm are explained, and then its population update mechanism is mathematically modeled in the form of two phases of exploration and exploitation.The exploration phase is designed to make extensive changes in the positions of the population members, which is inspired by the simulation of the customer choosing a design from a set of proposed designs and its implementation by the nail technician.In contrast, the exploitation phase is developed to make limited and targeted changes in the positions of the population members through a focused search with short steps, which reflects the nail technician's efforts to achieve the best performance and add creative details to the selected design.The efficiency of the proposed NTOA algorithm in solving optimization problems is evaluated using 29 standard benchmark functions of the CEC 2017 collection.In addition, the performance of NTOA is compared with the results of 9 well-known metaheuristic algorithms.The simulation results show that the NTOA algorithm has achieved the first rank of optimizer in 21 out of 29 benchmark functions (equivalent to 72.41%) and has provided competitive and acceptable performance in other functions.Also, the results of the Wilcoxon signed-rank test and the obtained p-values confirm the statistically significant superiority of the NTOA algorithm over the competing algorithms in the CEC 2017 test set.Overall, the simulation findings show that the NTOA algorithm, with its effective performance and statistically significant superiority, can be used as an efficient optimization tool in solving a wide range of optimization problems.

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