## 4 Comparison Experiments
### Comparison of Feature Selection Methods  
Feature selection finds the subset of features with the smallest dimensionality by retaining the primary information carried by all features. Four metaheuristic feature selection methods were selected for comparison to reflect the algorithmic quality of the EO. These included the classical particle swarm optimization (PSO) and genetic algorithm (GA), as well as the recently proposed pathfinder algorithm (PFA) and generalized normal distribution optimization (GNDO). Since comparing algorithms is a stochastic technique, a fair comparison using the same parameters is required. Experiments were carried out to confirm the effectiveness of feature selection on KNN using the joint feature set as the model input. The authors introduced 10-fold cross-validation to enhance the reliability and effectiveness of various classification models. Five independent repeat experiments were performed to test the stability.  
First, observe the algorithm's accuracy. Using a bar chart to depict the average accuracy and an error bar to indicate standard deviation, Figure 10 visually illustrates the comparison of the precision from different feature selection algorithms. As indicated in Figure 10, the accuracy of the features screened by the EO was higher than that of the remaining four algorithms, with an average accuracy of 94.27%. The accuracy of the other four algorithms ranged from 89.47% to 93.02%. The authors also noted that the EO had the best stability in screening features, with the lowest standard deviation of 0.7718%. Thus, the EO has an advantage over the classical (PSO, GA) methods in terms of accuracy and also over the algorithms newly proposed in recent years (PFA, GNDO).  
After comparing the accuracy, the focus was on the feature dimension of the screening. The convergence curve of the feature selection process was observed through 100 iterations. During the iterations, the error rate was adopted as the fitness function. The iterations are shown in Figure 11. The EO reduced the error rate from 0.11203 to 0.06431 after 83 iterations. The EO had the best adaptation compared to PSO, GA, PFA, and GNDO, indicating that it has better feature selection ability for industrial power loads. After completing the iterations, the feature selection dimensions of the different algorithms were recorded. Among them, the EO had 21 dimensions, the PSO had 35 dimensions, the GA  
Figure 9: Comparison of accuracy for different feature combinations.  
had 32 dimensions, the PFA had 34 dimensions, and the GNDO had 29 dimensions. The EO had a minor feature dimension, and a smaller feature dimension will also make the feature subset more efficient in terms of time cost.  
Therefore, the feature subset screened by the EO was optimal for the joint feature set, with the EO able to completely account for the relevance and redundancy when filtering features. It retained the primary information of the joint feature set and filtered out irrelevant information. Irrelevant information is often the cause of data analysis and model complexity. Interestingly, the authors also found that the feature attributes screened by different algorithms were more similar, with entropy value features having the highest probability of being selected. This indicated that the entropy value features contained richer information about the industrial equipment and were more effective for electric load identification. Therefore, the introduction of entropy value features and the use of the EO to screen features are efficient. The method described in this paper can be an excellent solution to the electric load identification problem in industrial power consumption scenarios.  
Figure 11: Comparison of iterations of different feature selection algorithms.  
Figure 10: Accuracy comparison of feature selection algorithms.