一个新的多混合差异演化算法,用于优化框架结构
Rohit Salgotra1,2, Amir H Gandomi3,4
1Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland. r.03dec@gmail.com.
Scientific reports
|February 28, 2024
概括
一个新的多混合差异演化 (MHDE) 算法通过提高性能而不会牺牲解决方案质量来增强计算智能. 这种新的方法为复杂的优化任务提供了更好的探索和利用.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 不同进化 (DE) 是一个强大的优化工具,用于复杂的问题.
- 现有的DE算法可以改进,以提高性能和效率.
研究的目的:
- 提出一个多混合DE (MHDE) 算法.
- 提高DE的工作能力和效率,而不会影响解决方案质量.
主要方法:
- 包括适应参数,增强突变和交叉,人口减少,代划分和高斯随机抽样.
- 使用韦布尔分布和高斯随机抽样来防止过早的收.
- 采用代分工,以改善勘探和开采.
主要成果:
- MHDE在IEEE CEC基准套件 (2005,2014,2017) 上得到了验证.
- 应用于四个工程设计问题和三个框架设计重量最小化问题.
- 在统计测试中表现优于最近的混合算法 (弗里德曼和威尔科克森的排名和值).
结论:
- 拟议的MHDE算法与现有方法相比,表现出优越的性能.
- 对于复杂的优化挑战,MHDE有效地平衡了勘探和开采.
- 该算法显示了工程设计和计算智能应用的巨大潜力.
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