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

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 expl...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2193-567X
1319-8025
0377-9211
2191-4281
DOI:10.1007/s13369-024-09694-z