Improved Weighted Chimp Optimization Algorithm Based on Fitness–Distance Balance for Multilevel Thresholding Image Segmentation
Guardado en:
| Udgivet i: | Symmetry vol. 17, no. 7 (2025), p. 1066-1103 |
|---|---|
| Hovedforfatter: | |
| Andre forfattere: | |
| Udgivet: |
MDPI AG
|
| Fag: | |
| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3233254141 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2073-8994 | ||
| 024 | 7 | |a 10.3390/sym17071066 |2 doi | |
| 035 | |a 3233254141 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Günay, Yılmaz Asuman |u Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon 61080, Türkiye | |
| 245 | 1 | |a Improved Weighted Chimp Optimization Algorithm Based on Fitness–Distance Balance for Multilevel Thresholding Image Segmentation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Multilevel thresholding image segmentation plays a crucial role in various image processing applications. However, achieving optimal segmentation results often poses challenges due to the intricate nature of images. In this study, a novel metaheuristic search algorithm named Weighted Chimp Optimization Algorithm with Fitness–Distance Balance (WChOA-FDB) is developed. The algorithm integrates the concept of Fitness–Distance Balance (FDB) to ensure balanced exploration and exploitation of the solution space, thus enhancing convergence speed and solution quality. Moreover, WChOA-FDB incorporates weighted Chimp Optimization Algorithm techniques to further improve its performance in handling multilevel thresholding challenges. Experimental studies were conducted to test and verify the developed method. The algorithm’s performance was evaluated using 10 benchmark functions (IEEE_CEC_2020) of different types and complexity levels. The search performance of the algorithm was analyzed using the Friedman and Wilcoxon statistical test methods. According to the analysis results, the WChOA-FDB variants consistently outperform the base algorithm across all tested dimensions, with Friedman score improvements ranging from 17.3% (Case-6) to 25.2% (Case-4), indicating that the FDB methodology provides significant optimization enhancement regardless of problem complexity. Additionally, experimental evaluations conducted on color image segmentation tasks demonstrate the effectiveness of the proposed algorithm in achieving accurate and efficient segmentation results. The WChOA-FDB method demonstrates significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) metrics with average enhancements of 0.121348 dB, 0.012688, and 0.003676, respectively, across different threshold levels (m = 2 to 12), objective functions, and termination criteria. | |
| 653 | |a Similarity | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Image segmentation | ||
| 653 | |a Computer vision | ||
| 653 | |a Color imagery | ||
| 653 | |a Fitness | ||
| 653 | |a Statistical tests | ||
| 653 | |a Multilevel | ||
| 653 | |a Task complexity | ||
| 653 | |a Optimization | ||
| 653 | |a Statistical methods | ||
| 653 | |a Solution space | ||
| 653 | |a Search algorithms | ||
| 653 | |a Image processing | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Entropy | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Signal to noise ratio | ||
| 700 | 1 | |a Samoua, Alsamoua |u Department of Software Engineering, Faculty of Technology, Karadeniz Technical University, Trabzon 61080, Türkiye; samoua.alsamoua@gmail.com | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 7 (2025), p. 1066-1103 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233254141/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233254141/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233254141/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |