High-dimensional benchmark test functions.

<div><p>Kernel Search Optimization (KSO) is characterized by insufficient accuracy in local search, which makes it difficult to achieve local optimization. Therefore, this paper proposes a Large Local Search Kernel Search Optimization (LLSKSO) to enhance the local optimization ability. L...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ruyi Dong (9038174) (author)
مؤلفون آخرون:	Ran Cui (542718) (author), Zhennao Cai (4524565) (author), Ali Asghar Heidari (11828696) (author), Lei Liu (5074) (author), Yanan Liu (114059) (author), Huiling Chen (9525) (author)
منشور في:	2025
الموضوعات:	Biophysics Medicine Biotechnology Infectious Diseases Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified introducing several strategies gaussian mutation strategy dung beetle optimizer dimensional benchmark functions experimental results obtained local optimization ability effective optimization algorithm greater strengths compared local search capability llskso algorithm outperforms achieve local optimization local search statistical results local traps global search paper proposes llskso achieves insufficient accuracy initial population homogenized using engineering problem dynamic balance comparison experiments baseline methods 20 high 10 well
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High-dimensional benchmark test functions.

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