Improvement of Wild Horse Optimizer Algorithm with Random Walk Strategy (IWHO), and Appointment as MLP Supervisor for Solving Energy Efficiency Problem

Abstract

This paper aims to enhance the success of the Wild Horse Optimization (WHO) algorithm in optimization processes by developing strategies to overcome the issues of stuckness and early convergence in local spaces. The performance change is observed through a Multi-Layer Perceptron (MLP) sample. In this context, an advanced Wild Horse Optimization (IWHO) algorithm with a random walking strategy was developed to provide solution diversity in local spaces using a random walking strategy. Two challenging test sets, CEC 2019, were selected for the performance measurement of IWHO. Its competitiveness with alternative algorithms was measured, showing that its performance was superior. This superiority is visually represented with convergence curves and box plots. The Wilcoxon signed-rank test was used to evaluate IWHO as a distinct and powerful algorithm. The IWHO algorithm was applied to MLP training, addressing a real-world problem. Both WHO and IWHO algorithms were tested using MSE results and ROC curves. The Energy Efficiency Problem dataset from UCI was used for MLP training. This dataset evaluates the heating load (HL) or cooling load (CL) factors by considering the input characteristics of smart buildings. The goal is to ensure that HL and CL factors are evaluated most efficiently through the use of HVAC technology in smart buildings. WHO and IWHO were selected to train the MLP architecture, and it was observed that the proposed IWHO algorithm produced better results.