利用改进的电鳗觅食优化技术优化量子 LSTM,用于真实世界的智能工程系统

IF 6 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
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引用次数: 0

摘要

元启发式方法与机器学习方法的结合具有显著优势,尤其是在优化和计算智能方面。这种融合利用了元启发式的全局搜索能力以及机器学习的模式识别和预测能力,有助于提高复杂问题空间的收敛速度和解决方案的质量。量子长短期记忆(QLSTM)是为解决此类复杂工程问题而量身定制的高效深度学习模型。QLSTM 的架构包括数据编码层、变异层和量子测量层,有助于有效编码和处理土木工程数据,从而提高预测精度。然而,由于其 NP 问题的性质和耗时的特点,确定 QLSTM 参数最佳值的任务面临着挑战。针对这一问题,我们提出了一种基于改进的电鳗觅食优化(MEEFO)的 QLSTM 优化替代技术。MEEFO 是原始 EEFO 的改进版,它应用三角突变算子来提高传统 EEFO 的搜索能力。因此,MEEFO 可优化 QLSTM 并提高其预测性能。为了验证我们提出的方法的有效性,我们利用五个与建筑和结构工程相关的实际工程数据集进行了综合实验。评估结果清楚地表明,MMEFO 显著提高了 QLSTM 的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized quantum LSTM using modified electric Eel foraging optimization for real-world intelligence engineering systems

The integration of metaheuristics with machine learning methodologies presents significant advantages, particularly in optimization and computational intelligence. This amalgamation leverages the global search capabilities of metaheuristics alongside the pattern recognition and predictive prowess of machine learning, facilitating enhanced convergence rates and solution quality in complex problem spaces. The Quantum Long Short-Term Memory (QLSTM) emerges as a highly efficient deep learning model tailored to tackle such intricate engineering problems. The QLSTM's architecture, comprising data encoding, variational, and quantum measurement layers, facilitates the effective encoding and processing of civil engineering data, leading to heightened prediction accuracy. However, the task of determining optimal values for QLSTM parameters presents challenges due to its NP-problem nature and time-consuming characteristics. To address this, we propose an alternative technique to optimize the QLSTM based on a modified Electric Eel Foraging Optimization (MEEFO). The MEEFO is a modified version of the original EEFO that applies triangular mutation operators to boost the search capability of the traditional EEFO. Thus, the MEEFO optimizes the QLSTM and boosts its prediction performance. To validate the efficacy of our proposed method, we conduct comprehensive experiments utilizing five real-world engineering datasets related to construction and structure engineering. The evaluation outcomes unequivocally demonstrate that the MMEFO significantly enhances the performance of the QLSTM.

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来源期刊
Ain Shams Engineering Journal
Ain Shams Engineering Journal Engineering-General Engineering
CiteScore
10.80
自引率
13.30%
发文量
441
审稿时长
49 weeks
期刊介绍: in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.
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