-dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.
|Number of pages||15|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Early online date||16 Jun 2023|
|Publication status||E-pub ahead of print - 16 Jun 2023|