Enhanced Adaptive Whale Optimization Algorithm for Kernel Extreme Learning Machine
Author Information
Author(s): Lin ZeSheng, Algamal Zakariya Yahya
Primary Institution: Vocational Training Center, FoShan Open University, FoShan, Guangdong Province, China
Hypothesis
Can the Enhanced Adaptive Whale Optimization Algorithm improve the performance of Kernel Extreme Learning Machine in classification tasks?
Conclusion
The Enhanced Adaptive Whale Optimization Algorithm significantly improves the accuracy and efficiency of data classification tasks using Kernel Extreme Learning Machine.
Supporting Evidence
- The Enhanced Adaptive Whale Optimization Algorithm outperformed standard Whale Optimization Algorithm in terms of fitness value and convergence speed.
- EAWOA demonstrated superior optimization accuracy compared to WOA across 21 test functions.
- The application of EAWOA significantly improved the accuracy and efficiency of data classification tasks.
Takeaway
This study shows a new way to help computers classify data better by using a special algorithm that mimics how whales hunt for food.
Methodology
The study used an Enhanced Adaptive Whale Optimization Algorithm to optimize the parameters of Kernel Extreme Learning Machine across 21 benchmark functions and 7 datasets.
Limitations
The algorithm requires resetting after each optimization process and has increased complexity, leading to higher computational demands.
Digital Object Identifier (DOI)
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