Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems

Hüseyin Bakır

doi:10.35377/saucis...1474767

EN

Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems

Abstract

This study presents the comparative performance analysis of Natural Survivor Method (NSM)-based algorithms in solving the IEEE CEC 2022 test suite benchmark problems and four real-world engineering design problems. Three different variants (Case1, Case2, Case3) of the NSM-TLABC, NSM-SFS and NSM-LSHADE-SPACMA algorithms were used in the study. The data obtained from the experimental studies were statistically analyzed using Friedman and Wilcoxon signed-rank tests. Based on the Friedman test results, NSM-LSHADE-SPACMA_Case2 showed the best performance with an average Friedman score of 3.96. The Wilcoxon signed-rank test showed that NSM-LSHADE-SPACMA_Case2 outperformed its competitors in 13 out of 16 experiments, achieving a success rate of 81.25%. NSM-LSHADE-SPACMA_Case2, which was found to be the most powerful of the NSM-based algorithms, is used to solve cantilever beam design, tension/compression spring design, pressure vessel design and gear train design problems. The optimization results are also compared with eight state-of-the-art metaheuristics, including Rime Optimization Algorithm (RIME), Nonlinear Marine Predator Algorithm (NMPA), Northern Goshawk Optimization (NGO), Kepler Optimization Algorithm (KOA), Honey Badger Algorithm (HBA), Artificial Gorilla Troops Optimizer (GTO), Exponential Distribution Optimization (EDO) and Hunger Games Search (HGS). Given that all results are together, it is seen that NSM-LSHADE-SPACMA_Case2 algorithm consistently produced the best results for the global and engineering design problems studied.

Keywords

References

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Details

Primary Language

English

Subjects

Empirical Software Engineering

Journal Section

Research Article

Authors

Hüseyin Bakır ^*
0000-0001-5473-5158
Türkiye

Early Pub Date

August 26, 2024

Publication Date

August 31, 2024

Submission Date

April 28, 2024

Acceptance Date

May 29, 2024

Published in Issue

Year 2024 Volume: 7 Number: 2

DOI

https://doi.org/10.35377/saucis...1474767

IZ

https://izlik.org/JA34RJ97BX

Cite

RIS / Bibtex

APA

Bakır, H. (2024). Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems. Sakarya University Journal of Computer and Information Sciences, 7(2), 227-243. https://doi.org/10.35377/saucis...1474767

AMA

1.Bakır H. Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems. SAUCIS. 2024;7(2):227-243. doi:10.35377/saucis.1474767

Chicago

Bakır, Hüseyin. 2024. “Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems”. Sakarya University Journal of Computer and Information Sciences 7 (2): 227-43. https://doi.org/10.35377/saucis. 1474767.

EndNote

Bakır H (August 1, 2024) Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems. Sakarya University Journal of Computer and Information Sciences 7 2 227–243.

IEEE

[1]H. Bakır, “Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems”, SAUCIS, vol. 7, no. 2, pp. 227–243, Aug. 2024, doi: 10.35377/saucis...1474767.

ISNAD

Bakır, Hüseyin. “Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems”. Sakarya University Journal of Computer and Information Sciences 7/2 (August 1, 2024): 227-243. https://doi.org/10.35377/saucis. 1474767.

JAMA

1.Bakır H. Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems. SAUCIS. 2024;7:227–243.

MLA

Bakır, Hüseyin. “Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems”. Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, Aug. 2024, pp. 227-43, doi:10.35377/saucis. 1474767.

Vancouver

1.Hüseyin Bakır. Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems. SAUCIS. 2024 Aug. 1;7(2):227-43. doi:10.35377/saucis. 1474767