Constrained pressure-vessel design optimization using the scuba diver optimization algorithm: parameter sensitivity and statistical validation

Authors

  • Saman M. Almufti Department of Information Technology, Technical College of Duhok, Duhok Polytechnic University, Duhok, Iraq, Department of Information Technology, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq.
  • Amira Bibo Sallow Department of Information Technology, Technical College of Duhok, Duhok Polytechnic University, Duhok, Iraq.

Keywords:

Constrained Engineering Optimization, Mixed-Discrete Optimization, Pressure-Vessel Design, Scuba Diver Optimization Algorithm, Parameter Sensitivity, Nonparametric Statistics.

Abstract

The pressure-vessel benchmark is a mixed-discrete nonlinear engineering problem in which shell and head thicknesses follow manufacturing increments while radius and cylindrical length remain continuous. This study applies the Scuba Diver Optimization Algorithm (SDOA) to the standard formulation and examines ten parameter configurations through 30 independent runs each. An oxygen-regulated state model assigns global exploration, moderate exploration, exploitation, fine tuning, or reset, while a repair procedure enforces the 0.0625-in thickness increment, variable bounds, minimum wall thicknesses, and required volume. All 300 runs ended with feasible designs. S7 achieved the best mean rank (4.000), the smallest standard deviation (0.055056), a mean cost of 6059.766062, and a 73.3% success rate within 0.001% of the proven optimum. S9 produced the closest individual design, with cost 6059.714482843 and a relative gap of 0.000002439%. Friedman and Holm-adjusted Wilcoxon analyses confirmed significant configuration effects. The results show that parameter selection mainly affects repeated-run consistency and search-stage allocation rather than attainable best-case accuracy.

Published

2026-07-23

How to Cite

Saman M. Almufti, & Amira Bibo Sallow. (2026). Constrained pressure-vessel design optimization using the scuba diver optimization algorithm: parameter sensitivity and statistical validation. International Journal of Information Technology & Computer Engineering , 6(1), 78–93. Retrieved from https://hmjournals.com/journal/index.php/IJITC/article/view/6508

Issue

Section

Aricle Publication

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