Enhancing the Potential of Random Forest Technique With an Adaptive Horse Herd Optimization Framework for Electric Load Prediction

The vast application of optimization algorithms has been observed in the field of machine learning for improving the accuracy of the model. A typical setback observed in optimization algorithms is the issue of premature convergence which might occur due to the absence of extraordinary exploration of the complex and continuous search space. Therefore, the present study attempts to introduce an imperative improvement in conventional horse herd optimization (HHO) which significantly boosts the optimizing capability by resolving the issue of premature convergence. This improved HHO (iHHO) is the integration of conventional HHO with the hunting and encircling characteristics of the conventional grey wolf optimization (GWO) algorithm. The performance of the proposed iHHO algorithm has been evaluated on standard benchmark functions and compared with conventional HHO, GWO, and other variants of basic variants of HHO. The statistical significance of the results is verified using the Friedman ranking and Wilcoxon pairwise tests, while computational analysis is demonstrated using Big‐O analysis. Later, the practical applicability of the proposed iHHO algorithm has been analyzed by optimizing the hyperparameters of random forest (RF) for developing the effective electric load forecasting model. The error matrices evaluation, stability test, and the Diebold–Mariano test have been performed to highlight the superiority of the iHHO optimized RF model. Furthermore, the performance validation has been extended by comparing the present state of art with the other recently developed models.