将反对Nelder–Mead算法集成到遗传算法的选择阶段以实现增强优化

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Farouq Zitouni, Saad Harous
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引用次数: 0

摘要

在本文中,我们提出了一种新的方法,结合了对立的Nelder-Mead算法和遗传算法的选择阶段。这种整合旨在提高整体算法的性能。为了评估我们方法的有效性,我们对11种最先进的算法进行了全面的比较研究,这些算法以其在2022年IEEE进化计算大会(CEC 2022)上的卓越表现而闻名。经过严格的分析,包括弗里德曼测试和随后的邓恩事后测试,我们的算法表现出出色的性能。事实上,在大多数情况下,与其他算法相比,我们的方法表现出相同或更好的性能。这些结果突出了我们提出的方法的有效性和竞争力,展示了它在解决优化问题方面实现最先进性能的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization
In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.
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来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
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