# Improvement Technique for Group Search Optimization Using Experimental Design Method
## 1 Introduction
Because of its dynamic search characteristics, which mimic the adaptation of animals to their natural environment, and its convenient practical application, SIO is used to solve many practical problems in various fields, including scheduling problems [21; 22], benchmark function problems [23], industrial applications [24; 25; 26], and vehicle fuel efficiency [27]. The typical and high-profile SIOs include PSO, GSO, and others. In nature, many animal species are gregarious, such as humans, birds, ants, bees, etc. Information sharing is a common phenomenon observed in the social behavior of gregarious animals. For example, most actions of an individual in a group of gregarious animals are made not only after relevant information is shared among individuals in the group but also after relevant information links are shared among individuals in the group. Currently, the two most widely discussed theoretical frameworks of group sharing are information share and producer-scrounger (PS) [28]. The GSO algorithm used in this paper is the SIO algorithm proposed by [PERSON] et al. [8]. The SIO algorithm is inspired by gregarious behavior observed in animals. To design the updated formula, the proposed GSO algorithm simulates the foraging behavior of animals in nature. Specifically, the GSO introduces a food search mechanism for use in updating the formula. Additionally, the GSO applies a PS model of animals in the foraging process for use in developing search strategies. Despite the many successful applications of GSO reported so far, its shortcoming is its search performance. Specifically, GSO has low accuracy and tends to become trapped in the local optima. As a result, GSO has poor robustness. [PERSON] [29] organized the improved GSO methods proposed from 2009 to 2018. [PERSON] and [PERSON] [30] used a quasi-oppositional GSO to transfer the search space. [PERSON] [31] used an adaptive GSO to improve various mechanisms of the producer, scrounger, and ranger. [PERSON] et al. [26] used a three-dimensional GSO to re-design the distribution of producers, scroungers, and rangers; [PERSON] and [PERSON] [25] took a chaotic binary GSO to integrate chaotic maps with the binary GSO algorithm; using a modified GSO [32] to generate a decomposition strategy for the multi-objective optimization problem. [PERSON] et al. [33] augmented and ranked the producers if the improvement was not good after ten iterations. The other members were considered followers and divided into four groups to follow different producers to expand the search range. [PERSON] and [PERSON] [34] proposed 3D-GSO which integrate continuous, binary, and integer search spaces to improve the performance. [PERSON] et al. [35] proposed an improved boundary search strategy in GSO to heating pipeline system. Despite the reported performance improvements obtained by these improved methods and strategies, their limitations include problems in coordinating exploration and development operations. Additionally, their search results for the optimal combination of parameters often fall into partiality. That is, further study is needed to address issues such as extremum and poor robustness of results. The process of finding the best combination of parameters involves experimentation and the verification of results. Currently, the most commonly used method is the trial-and-error method [36], which is a non-systematic method that may not necessarily find the best combination of parameters. Thus, this paper uses a uniform design (UD) [37] method that prioritizes uniform dispersion at the expense of orderliness and comparability. Therefore, UD reduces the number of experiments required to obtain valuable data and to find the best parameter combination. Although UD is well established as an effective systematic experimental design method, this study applied the regional search characteristics of Taguchi method to further improve the robustness of UD results [38; 39].