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基于MWOA-BiLSTM算法的机器故障检测模型

Yi-Qiang Xia¹, Yang Yang¹

¹College of Science, Liaoning Technical University, Fuxin, China.

|November 11, 2024

概括

调制鱼优化算法 (MWOA) 通过防止过早的融合来增强元启发式优化. MWOA-BiLSTM在分类任务中表现出卓越的性能,优于其他方法.

科学领域:

计算智能是一种计算智能.
超启发式优化优化
生物学启发的算法

背景情况:

经典的鱼优化算法 (WOA) 面临着局部最佳和过早融合的挑战.
现有的元启发算法往往难以有效地平衡探索和利用.
生物学启发的优化技术为改善问题解决提供了潜力.

研究的目的:

引入调制鱼优化算法 (MWOA) 进行增强的元启发性优化.
解决传统算法的局限性,特别是局部最佳和过早的融合.
评估MWOA的性能及其在分类任务中的应用.

主要方法:

开发模块化鱼优化算法 (MWOA),包括收缩环绕和螺旋位置更新.
整合考奇变量和扰动项,以改善搜索空间探索.
使用CEC2005基准函数和威尔科克森等级总和测试进行比较分析.
应用MWOA与BiLSTM分类器 (MWOA-BiLSTM) 结合使用,用于绩效评估.

主要成果:

MWOA有效地克服了局部最佳和过早的融合问题.
比较研究表明,MWOA的表现优于最近七个关于CEC2005基准函数的元启发.
威尔科克森等级总和测试证实了MWOA有效性的统计学意义.
与其他元启发式-BiLSTM组合相比,MWOA-BiLSTM实现了卓越的准确性,精度,回忆和F1-Score.

结论:

MWOA展示了强大的优化能力,有效地平衡勘探和开采.
拟议的MWOA是metaheuristic优化的一个重大进步.
MWOA-BiLSTM为分类问题提供了一种非常有效的方法,其性能优于现有的方法.

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