Parameter settings for metaheuristic algorithm.

<div><p>This paper analyzes the shortcomings of the traditional Whale Optimization Algorithm (WOA), mainly including the tendency to fall into local optima, slow convergence speed, and insufficient global search ability for high-dimensional and complex optimization problems. An improved...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yanzhao Gu (21192659) (author)
مؤلفون آخرون:	Junhao Wei (6816803) (author), Zikun Li (2460040) (author), Baili Lu (21192662) (author), Shirou Pan (21568325) (author), Ngai Cheong (21192665) (author)
منشور في:	2025
الموضوعات:	Biophysics Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shows great potential longer computation times experimental results showed excellent woa variants enhanced prey encircling cosine search strategies comparative scalability experiment adaptive parameter adjustment pressure vessel design global search ability engineering optimization problems demonstrating stronger stability compositional optimization problems complex optimization problems gwoa significantly improves basic metaheuristic algorithms slow convergence speed may also fall optimization ability convergence speed scale problems spring design solution stability may lead convergence efficiency xlink "> test functions strategy integration solution accuracy several experiments paper analyzes overall efficiency met constraints local optima latest algorithms handling large efficient tool covering unimodal 46 %.
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Parameter settings for metaheuristic algorithm.

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