Szczegóły publikacji
Opis bibliograficzny
A low-level teamwork hybrid model based swarm intelligent algorithm for engineering design optimization / Amanjot Kaur Lamba, Rohit SALGOTRA, Nitin Mittal // Computer Methods in Applied Mechanics and Engineering ; ISSN 0045-7825 . — 2025 — vol. 447 art. no. 118317, s. 1–68. — Bibliogr. s. 65–68, Abstr. — Publikacja dostępna online od: 2025-09-01. — R. Salgotra - dod. afiliacja: Faculty of Engineering & IT, University of Technology Sydney, New South Wales, Australia
Autorzy (3)
- Kaur Lamba Amanjot
- AGHSalgotra Rohit
- Mittal Nitin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162341 |
|---|---|
| Data dodania do BaDAP | 2025-09-12 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.cma.2025.118317 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computer Methods in Applied Mechanics and Engineering |
Abstract
We introduce a multi-algorithm hybrid strategy, named WIFN, to mitigate the poor performance of the naked mole-rat algorithm (NMRA). The proposed WIFN algorithm employs the best exploration and exploitation properties of existing algorithms, viz. weighted mean of vectors (INFO), whale optimization algorithm (WOA) and fission fusion optimization (FuFiO). These algorithms are integrated into the worker phase of the NMRA. A new stagnation phase is introduced in WIFN to minimize the effect of local optima stagnation. To add self-adaptivity, five new mutation/inertia weight strategies are added to the parameters of WIFN. To assess its performance, four data sets are used: classical benchmarks, CEC 2014, CEC 2017 and CEC 2019. An experimental study is carried out using i) five constrained engineering design problems and ii) 15 real-world constrained problems from the CEC 2020 benchmark dataset to analyze the applicability of WIFN for computationally expensive problems. In addition, WIFN is applied to multilevel image thresholding with type-II fuzzy sets. It is tested using a real image set that features different histogram distributions for three different threshold numbers. Experimental results suggest that WIFN perform significantly better than the existing state-of-the-art algorithms in terms of quality metrics, viz. mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Wilcoxon's ranksum and the Friedman test establish the superiority of WIFN statistically.
