Volume 35, Issue 1 (IJIEPR 2024)
IJIEPR 2024, 35(1): 1-14 |
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1- Department of Operations Research, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt. , Rabee.saleh86@gmail.com
2- Department of Statistics, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt.
3- Department of Operations Research, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt.
2- Department of Statistics, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt.
3- Department of Operations Research, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt.
Abstract: (1129 Views)
Harris Hawks Optimization (HHO) algorithm, which is a new metaheuristic algorithm that has shown promising results in comparison to other optimization methods. The surprise pounce is a cooperative behavior and chasing style exhibited by Harris' Hawks in nature. To address the limitations of HHO, specifically its susceptibility to local optima and lack of population diversity, a modified version called Modified Harris Hawks Optimization (MHHO) is proposed for solving global optimization problems. A mutation-selection approach is utilized in the proposed Modified Harris Hawks Optimization (MHHO) algorithm. Through systematic experiments conducted on 23 benchmark functions, the results have demonstrated that the MHHO algorithm offers a more reliable solution compared to other established algorithms. The MHHO algorithm exhibits superior performance to the basic HHO, as evidenced by its superior average values and standard deviations. Additionally, it achieves the smallest average values among other algorithms while also improving the convergence speed. The experiments demonstrate competitive results compared to other meta-heuristic algorithms, which provide evidence that MHHO outperforms others in terms of optimization performance.
Keywords: Metaheuristics optimization algorithms, Nature-inspired algorithms, Harris Hawks algorithm, Evolutionary Algorithms, Global optimization problems.
Type of Study: Research |
Subject:
Optimization Techniques
Received: 2023/07/21 | Accepted: 2024/01/23 | Published: 2024/03/11
Received: 2023/07/21 | Accepted: 2024/01/23 | Published: 2024/03/11
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