Scalability of the proposed IFOX on C17F8 with the following metrics: Runtime (seconds), Memory usage (MB), and Relative error across problem dimensions (10, 30, 50, 100) and epochs (50, 100, 250, 500, 1000), averaged over 30 independent runs.

<p>Scalability of the proposed IFOX on C17F8 with the following metrics: Runtime (seconds), Memory usage (MB), and Relative error across problem dimensions (10, 30, 50, 100) and epochs (50, 100, 250, 500, 1000), averaged over 30 independent runs.</p>

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Bibliographic Details
Main Author:	Mahmood A. Jumaah (22272371) (author)
Other Authors:	Yossra H. Ali (22272374) (author), Tarik A. Rashid (6519329) (author)
Published:	2025
Subjects:	Neuroscience Ecology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified removing four parameters experimental results showed dynamically scaled step art optimization algorithms solving many real parametric statistical tests effectively balancing exploration achieved 880 wins overall performance metrics fox optimization algorithm exploitation often limit ifox ), incorporating ten real rank tests ifox achieved cec ), balance exploration wilcoxon signed size parameter significant potential primary equations paper introduces original fox local minima involved functions improved variation getting trapped fitness value findings highlight exploitation based evolutionary computation average rank 348 losses 228 ties
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Scalability of the proposed IFOX on C17F8 with the following metrics: Runtime (seconds), Memory usage (MB), and Relative error across problem dimensions (10, 30, 50, 100) and epochs (50, 100, 250, 500, 1000), averaged over 30 independent runs.

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