Abstract
Abstract
Engineering and science have increasingly used metaheuristic algorithms to solve actual optimization problems. One of the challenging problems is the proper selection of the parameters of photovoltaic cells since these cells are a great source of clean energy. For such difficult situations, the Harris Hawks Optimization method can be a useful tool. However, HHO is susceptible to the local minimum. This study suggests a novel optimizer called Enhanced Exploration and Exploitation Harris Hawks Optimization using Logarithms, Exponentials, and Travelled Distance Rate (E3H2O-LE-TDR) algorithm, which is a modified version of HHO. The algorithm proposed in this study emphasizes the utilization of random location-based habitats during the exploration phase and the implementation of strategies 1, 3, and 4 during the exploitation phase. In the proposed algorithm, Harris hawks in the wild will change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. Therefore, the cons of the original HHO have been solved. Furthermore, E3H2O-LE-TDR was also tested across multiple benchmarks to prove its credibility and efficacy. The approach was tested on the CEC2017, CEC2019, CEC2020, and 27 other benchmark functions with different modalities. The suggested approach is also evaluated on six traditional real-world engineering situations. E3H2O-LE-TDR is compared to state-of-the-art algorithms, as well as other modifications of HHO. The numerical results show that the proposed algorithm outperforms all its competitors, which is visually proven using different convergence curves. Furthermore, the results of the mean Friedman rank statistical test proved the superiority of the proposed algorithm. The results for the single and double diode pv cell model, E3H2O-LE-TDR presented the best performance as indicated by the absolute error in both the current and power values for different operating conditions.
Publisher
Research Square Platform LLC