A Comparative Study of Metaheuristic Optimization Algorithms in Solving Engineering Designing Problems

Widi Aribowo; Hisham A. Shehadeh

doi:10.18196/jrc.v6i4.26410

Authors

Widi Aribowo Universitas Negeri Surabaya https://orcid.org/0000-0002-4059-1293
Hisham A. Shehadeh Al-Balqa Applied University https://orcid.org/0000-0003-4054-6561

DOI:

https://doi.org/10.18196/jrc.v6i4.26410

Keywords:

Swarm Based Optimization Algorithms, Physical Based Optimization Algorithm, Speed Reducer Design, Pressure Vessel Design

Abstract

This paper presents a comprehensive comparative study of several metaheuristic optimization algorithms with the aim of identifying the most effective method for solving well-established engineering design problems. The algorithms selected for this study include Sperm Swarm Optimization (SSO), Chernobyl Disaster Optimizer (CDO), Bermuda Triangle Optimizer (BTO), Marine Predators Algorithm (MPA), and Particle Swarm Optimization (PSO). These algorithms are tested and evaluated through both qualitative and quantitative analyses.The first phase of testing involves applying the algorithms to a set of benchmark functions from the Congress on Evolutionary Computation (CEC) 2017 suite. Key performance indicators such as best fitness value, standard deviation, and mean are used to measure solution quality, while convergence curves are analyzed to assess optimization efficiency over iterations. This allows for a robust evaluation of each algorithm's ability to balance exploration and exploitation in the search space. In the second phase, the algorithms are implemented to solve real-world engineering design problems, including Speed Reducer Design, Pressure Vessel Design, Cantilever Beam Design, and Robot Gripper Optimization. These case studies further validate the practical applicability and versatility of the algorithms in handling complex, multidimensional, and constrained optimization tasks. The results indicate varying levels of performance across different problems, highlighting the strengths and limitations of each method. This comparative insight provides valuable guidance for researchers and practitioners in selecting suitable optimization techniques for specific engineering challenges.

Author Biographies

Widi Aribowo, Universitas Negeri Surabaya

Widi Aribowo is a lecturer in the Department of Electrical Engineering, Universitas Negeri Surabaya, Indonesia. He is B.Sc. in Power Engineering, Sepuluh Nopember Institute of Technology (ITS)-Surabaya in 2005. He is M.Eng. in Power Engineering, Sepuluh Nopember Institute of Technology (ITS)-Surabaya in 2009. He mainly research in the power systems, metaheuristic algorithms, and control. He can be contacted at email: widiaribowo@unesa.ac.id.

Hisham A. Shehadeh, Al-Balqa Applied University

isham A. Shehadeh received the B.S. degree in computer science from Al-Balqa` Applied University, Jordan, in 2012, the M.S. degree in computer science from the Jordan University of Science and Technology, Irbid, Jordan, in 2014, and the Ph.D. degree from the Department of Computer System and Technology, University of Malaya (UM), Kuala Lumpur, Malaysia, in 2018. He was an Assistant professor of CS/AI in Amman Arab University from 2020 to 2024. He was a research assistant at UM from 2017 to2018. He was a Teaching Assistant and a lecturer with CS Department, College of Computer and Information Technology, Jordan University of Science and Technology from 2013 to 2014 and from 2014 to 2016 respectively. Currently, He is an Assistant Professor of Computer Science at the Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Jordan. His current research interests are, intelligent computing, metaheuristic algorithms and algorithmic engineering applications of WSN.

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