# Improvement Technique for Group Search Optimization Using Experimental Design Method
## 1 Introduction  
Rapid progress in the performance of various computer hardware components has substantially increased the overall computing power available to researchers. As a result of the increasing capability to analyze massive amounts of data and their relationships, many new artificial intelligence models have recently emerged. The capability to construct useful mathematical models of real-world problems has accelerated scientific progress in recent decades by enabling researchers to perform experimental simulations rapidly, repeatedly, and at a low cost. However, a continuing challenge for researchers performing simulations is finding the optimal set of experimental parameters. Methods commonly used to solve the parameter optimization problem can be classified into three types: enumeration methods, numerical methods, and random search methods [1]. Of the enumeration methods, the most common is grid search, which divides the search space into levels and searches each level individually. Although a grid search can find the best solution for the entire domain,its limitation is its high consumption of computing resources. Therefore, enumeration methods are the least attractive methods for solving problems involving a very large search space. The second type of method used to solve parameter optimization problems, numerical methods, use the mathematical derivative method to find the extremum in the search space. For example, the backward propagation method used in artificial neural networks is based on the gradient descent method [2] used to find the best parameters. A major disadvantage of numerical methods, however, is that in a discontinuous and non-smooth search space, the ease of optimizing the local solution tends to cause premature convergence. Therefore, numerical methods are best suited for solving optimization problems in which the target values are smooth and precise. The third type of parameter optimization method is the random search method, which is widely used today. Random search methods simulate the behavior of living organisms by establishing a mathematical model or function and then performing a random number search in the search space to find the best solution in the entire domain. [PERSON] and [PERSON] summarized common random search methods, including biology-based, swarm-based, physics-based, social-based, music-based, chemistry-based, sports-based, mathematics-based, and hybrid methods [3]. Among these methods, popular methods include biology-based methods, such as genetic algorithm (GA) [4] and immune algorithm (IA) [5], and swarm-based methods, such as ant colony optimization (ACO) [6], particle swarm optimizer algorithm (PSO) [7], group search optimization (GSO) [8], and whale optimization algorithm (WOA) [9]. These methods have been applied to healthcare [10; 11], antenna design [12; 13], power planning [14; 15], water resource [16; 17], the semiconductor industry [18; 19], etc. The swarm intelligence optimizer (SIO) method [20] is an algorithm that imitates the behavior of biological groups. This concept is based on cooperation among many intelligent individuals to present living beings' behavior. The method predicts outcomes by mimicking the behaviors of intelligent individuals in biological groups observed in nature. That is, the SIO performs optimization through a process of constructing and updating a random search algorithm based on the behavior of gregarious creatures in nature. In this process, the updated position of an individual in the search space is analogous to an individual animal, and the fitness value of the benchmark function is analogous to the survivability of an individual animal in nature. If the fitness value of an individual can be increased, the survivability of the individual can be increased. That is, a better solution can be discovered.