An Improved Multi-Objective Harris Hawks Optimization Algorithm for Solving EDM Problems

• Published in: Arabian Journal for Science and Engineering Vol. 50; no. 15; pp. 12403 - 12448
• Main Authors: Uddin, Md Piyar, Majumder, Arindam, Barma, John Deb, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025; Springer Nature B.V
• Subjects: Algorithms; Archives & records; Benchmarks; Composite materials; Engineering; Genetic algorithms; Humanities and Social Sciences; Integrated approach; multidisciplinary; Multiple objective analysis; Optimization; Optimization algorithms; Optimization techniques; Performance enhancement; Research Article-Computer Engineering and Computer Science; Science; Global optimization; Harris hawks optimization algorithm; Multi-objective optimization; Pareto optimal set; EDM
• ISSN: 2193-567X; 1319-8025; 0377-9211; 2191-4281
• DOI: 10.1007/s13369-024-09694-z

In this study, an improved multi-objective Harris hawks optimization algorithm (IMHHO) is developed to solve optimization problems related to the EDM process. In the IMHHO algorithm, an exponentially decreasing strategy is applied to update the escaping energy. The candidate’s selection for the exploration phase of the improved algorithm is carried out using a newly proposed crowding distance-based approach. For sorting the non-dominated solutions, the algorithm implemented the fast non-dominated sorting technique adopted from NSGA-II. The proposed algorithm is tested on eleven widely used benchmark problems and ten existing instances of the EDM process. Both unconstrained and constrained benchmark problems are considered for this study. The performance of the algorithm is measured in terms of coverage, spacing, and CPU time. A comparison of IMHHO with existing MHHO and four recently developed population-based optimization techniques is then performed to evaluate the algorithm’s capability. The results and findings demonstrate IMHHO as better than MHHO, with an average improvement of 83.94%, 38.06%, and 79.53% in terms of coverage, spacing, and CPU time, respectively. The IMHHO, MMRO, and MSMA results show improved coverage, spacing, and CPU time of IMHHO with an average performance enhancement of 48.68%, 51.30%, and 91.28%, respectively, compared to the other two algorithms. The comparison of IMHHO with MBO and MCSA reveals an overall comparable performance in terms of coverage and average superiority of 56.96% and 98.36% in spacing and CPU time, respectively.