الفهرس 
chapter 1Only 14 pages are availabe for public view
 |  | from | 193 |  |  |
| | | from | 193 | | |
Abstract
Engineering and science have increasingly used metaheuristic algorithms to solve actual optimization problems. For difficult situations, the Harris Hawks Optimization (HHO) method can be a useful tool. HHO, on the other hand, is susceptible to the local minimum. This thesis suggests Improved Harris Hawks Optimization (IHHO), a modified version of HHO. The algorithm proposed in this thesis 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 will change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. The IHHO Algorithm was tested across multiple benchmarks to prove its credibility and efficacy. The approach was tested on the standard benchmark functions, CEC2017, CEC2019, CEC2020, and 52 other benchmark functions. The suggested approach is also evaluated on six traditional real-world engineering situations. For application of the algorithm in real world problems, IHHO is applied for estimating the parameters of various installed PV systems. In this study, IHHO has been compared to five established and recently developed algorithms: Grey Wolf Optimization (GWO), BAT algorithm, teaching-learning-based optimization (TLBO), Moth-flame optimization (MFO), and Whale Optimization Algorithm (WOA), as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). The numerical results show that the proposed algorithm IHHO outperforms GWO, BAT, WOA, TLBO, and MFO, which is visually proven using different convergence curves and three other modifications of HHO (BHHO, LogHHO, and MHHO) for which the solution cannot be reached globally except in some benchmark problems, but IHHO was able to overcome them and achieve the global solution in most benchmarks. A mean Friedman rank statistical test compares the IHHO rank to different methods. The recommended method outperformed GWO, BAT, WOA, TLBO, MFO, and three HHO versions (BHHO, LogHHO, and MHHO) in the Friedman test.