Szczegóły publikacji
Opis bibliograficzny
A hybrid swarm intelligence algorithm for region-based image fusion / Rohit SALGOTRA, Amanjot Kaur Lamba, Dhruv Talwar, Dhairya Gulati, Amir H. Gandomi // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322. — 2024 — vol. 14 art. no. 13723, s. 1-37. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 34-37, Abstr. — Publikacja dostępna online od: 2024-06-14. — R. Salgotra - dod. afiliacja: MEU Research Unit, Middle East University, Amman, Jordan ; University of Technology Sydney, Ultimo, Australia
Autorzy (5)
- AGHSalgotra Rohit
- Kaur Lamba Amanjot
- Talwar Dhruv
- Gulati Dhairya
- Gandomi Amir H.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 153881 |
|---|---|
| Data dodania do BaDAP | 2024-06-26 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41598-024-63746-w |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Reports |
Abstract
This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index (QAB/F), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure (NAB/F). The average QAB/F=0.765508, SCD=1.63185, SSIM=0.726317 and NAB/F=0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.
