Application of the Improved Whale Optimization Algorithm in Random Forest Parameter Tuning

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Published: 2026-01-20
Keywords:
Improved Whale Optimization Algorithm, Random Forest, hyperparameter search, UCI datasets, inertia weight, step-size update, classification accuracy

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Oliver Bennett
School of Computing, University of Leeds, Leeds, LS2 9JT, United Kingdom
Hannah Collins
School of Computing, University of Leeds, Leeds, LS2 9JT, United Kingdom
Samuel Turner
School of Computing, University of Leeds, Leeds, LS2 9JT, United Kingdom

Abstract

Random Forest models rely on well-chosen hyperparameters, but common search methods often run slowly and give uneven results across datasets. This study introduces an Improved Whale Optimization Algorithm (IWOA) that updates inertia weight and step size during the search. The method was tested on twelve UCI classification datasets and compared with standard WOA-RF, PSO-RF, and DE-RF. IWOA-RF increased mean accuracy by 3.8% over WOA-RF and lowered training time by 11%. It also showed steadier results across repeated runs. The convergence curves showed that IWOA reached good solutions in fewer iterations. These results suggest that small changes in the update rules can make WOA more suitable for tuning RF models. The study also notes that only classification tasks were tested, and future work should include regression datasets and more complex RF settings.

Article Details

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Vol. 1 No. 2 (2025): 2025 International Conference on Computer Science, Artificial Intelligence and Communication Systems (CSACS 2025)

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Articles

How to Cite

Application of the Improved Whale Optimization Algorithm in Random Forest Parameter Tuning. (2026). Journal of Science, Innovation & Social Impact, 1(2), 155-160. https://sagespress.com/index.php/JSISI/article/view/69

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