基于改进的人工蜂群多层感知算法的混凝土压力强度预测
Ping Li1, Yanru Zhang2, Jiming Gu1
1School of Mechanical Engineering, Anhui University of Technology, Ma'anshan, 243032, China.
Scientific reports
|March 18, 2024
概括
改进的人工蜂群算法 (IABC) 与多层感知子 (MLP) 结合,可以提高混凝土压力强度的预测. 这种IABC-MLP模型通过更好地捕捉复杂的非线性关系,提供了比传统方法更高的精度.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算智能是一种计算智能.
- 材料科学 材料科学 材料科学
背景情况:
- 预测混凝土的压力强度是一个复杂的非线性问题,受众因素的影响.
- 现有的经验公式由于有限的实验数据总结而缺乏普遍性.
- 传统模型难以准确地捕捉多种影响因素的复合效应.
研究的目的:
- 开发一个改进的人工蜂群算法 (IABC) 来优化多层感知子 (MLP) 模型.
- 为准确的混凝土压力强度预测提出一个新的IABC-MLP合模型.
- 评估IABC-MLP模型的性能与现有的预测方法相比.
主要方法:
- 开发了一种改进的人工蜜蜂群算法 (IABC),该算法包含了高斯变异运算符.
- 使用IABC算法 (IABC-MLP) 设计和优化了一个多层感知子 (MLP) 神经网络.
- 将IABC-MLP模型的性能与ABC-MLP,PSO-MLP,MLP,决策树,支持向量机回归和随机森林算法进行了比较.
主要成果:
- 与基本的ABC算法相比,IABC算法证明了改进的融合速度和避免局部最佳值.
- 与ABC-MLP和PSO-MLP模型相比,IABC-MLP模型实现了更高的训练精度 (1.6%和4.5%的增加).
- 拟议的IABC-MLP模型在所有评估指标上表现出色,超过了个别学习算法.
结论:
- 该IABC-MLP合模型有效地捕捉了混凝土压力强度预测中的复杂非线性关系.
- 集成IABC显著提高了MLP模型的训练和预测准确性.
- 启发式算法,特别是拟议的IABC-MLP,为预测混凝土压力强度提供了一种卓越的方法.
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