## 2 Related Works
### Related Works on GEO  
GEO is widely utilized by researchers, as it is an outstanding meta-heuristic method. [PERSON] et al. [27] presented a two-stage photovoltaic residential system with electric vehicle charging capability, where the performance of the system was improved by an optimized proportional and integral gain selection bidirectional DC/DC converter (BDC) proportional-integral controller via the GEO algorithm. The golden eagle algorithm also outperformed the particle swarm and genetic algorithms in terms of performance. [PERSON] et al. [28] presented improved grid-connected wind turbine performance using PI control strategies based on GEO to improve the dynamic and transient stability of grid-connected PMSG-VSWT. In addition, GEO has also been applied to fuzzy control. [PERSON] et al. [29] set out to improve the performance of a nonlinear power system using a hierarchical golden eagle architecture with a self-evolving intelligent fuzzy controller (HGE-SIFC). GEO seems to be popular in wind and power systems. [PERSON] et al. [30] accurately predicted wind power generation by means of the GEO algorithm, which they first improved, before proposing an extreme learning machine model that combined the improved GEO with an extreme learning machine for the prediction of wind power from numerical weather forecast data. In solar photovoltaic power systems, the GEO algorithm has another predictive feature. [PERSON] et al. [31] addressed the instability of machine learning and combined the GEO  
Figure 2: The relationship between A, C and the hyperplane. with a machine learning model, proposing a new machine learning model that yielded a smaller minimum root mean square error than the comparison model. Huge electricity demands have proved to be a tough challenge for power companies and system operators due to the increasing number of consumers in the electricity system and the unpredictability of electricity loads. Therefore, [PERSON] et al. [32] proposed an effective energy management system (EMS) named golden eagle optimization with incremental electricity conductivity (GEO-INC) to meet demands with respect to load. [PERSON] et al. [33] proposed an improved GEO with improved strategies including an individual sample learning strategy, a decentralized foraging strategy and a random perturbation strategy, and these improved strategies were applied to a hybrid energy storage system with a complementary wind and solar power storage system for energy optimization. GEO has also been used in forecasting using learning machines; for example, a meta-learning extreme learning machine (MGEL-ELM) based on GEO and logistic mapping, and a same-date time-interval-averaging interpolation algorithm (SAME) [34] have been proposed in the literature to improve the forecasting performance of incomplete solar irradiance time series datasets. Profit Load Distribution (PLD) is a typical multi-constrained nonlinear optimization problem, and is an important part of energy saving and consumption reduction in power systems. Group intelligent optimization algorithms are an effective method for solving nonlinear optimization problems such as PLD. For the actual operational constraints of the power system in the PLD model, a novel GEO-based solution was proposed in [35]. A series of segmented quadratic polynomial sums were modelled for the fitness function as the cost function used to calculate the optimization by the first GEO. The results showed that the GEO was able to effectively solve the power system PLD problem. In addition to the prediction and power system aspects, GEO has also been applied in the field of image research. [PERSON] et al. [36] applied GEO to the disc segmentation of human retinal images using pre-processing based on the golden eagle algorithm-guided geometric active Shannon contours and post-processing based on regular interval cross-sectional segmentation. [PERSON] et al.