Performance comparisons of MGFOA with state-of-the-art competitors on the IEEE CEC 2017.

<p>Performance comparisons of MGFOA with state-of-the-art competitors on the IEEE CEC 2017.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yang Gao (18005) (author)
مؤلفون آخرون:	Liang Cheng (228343) (author)
منشور في:	2025
الموضوعات:	Medicine Ecology Infectious Diseases Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified well control parameters including ablation studies ieee cec 2022 ieee cec 2017 algorithm &# 8217 growing global demand greedy selection mechanism achieving effective optimization search trace visualizations population &# 8217 mgfoa significantly outperformed first apply multi gas production optimization gas production global optimization swarm mechanism optimization performance similar algorithms search ability scalability experiments powerful tools petroleum sector paramount importance paper proposes gradient information evolutionary algorithms economic success development potential development capabilities comparable algorithms biological evolution
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