In this work, an intelligent multi-objective optimization technique is proposed to optimize various parameters such as power consumption ($P_c$), computation time ($C_t$) and area ($A$) for a health monitoring system designed using the Secured Dynamic Partial Reconfiguration (SDPR) architecture. The cost-efficient Dynamic Partial Reconfiguration (DPR)-based field-programmable gate array (FPGA) is very useful for analyzing many applications such as automation and data processing. The novelty of this paper is to design a new intelligent SDPR (i-SDPR) module in order to achieve better performance in a health monitoring system by considering various performance parameters. The SDPR architecture is incorporated with two reconfigurable modules such as Encrypt and Authenticate, which results in an increase in the core power consumption ($P_c$), computation time ($C_t$) and area ($A$). So, to improve the performance of the SDPR-based health monitoring system, there is a necessity to optimize the performance parameters and it is achieved through intelligent multi-objective evolutionary (MOEA) techniques. The intelligent multi-objective evolutionary techniques such as Niched-Pareto Genetic Algorithm (NPGA), Pareto Archived Evolution Strategy (PAES) and Pareto Envelope-based Selection Algorithm (PESA) have been considered for better optimization in the performance parameters. For the study, the free-to-use MIMIC-III dataset is taken, which contains critically admitted various intensive care unit patient data. The dataset is processed through any one of the multi-objective evolutionary operators till satisfying the conditions and then forwarded to implementation. The proposed architecture has been implemented and tested using Cyclone$Ⓡ$ V SX SoC Development Kit. The comparative analysis of various performance parameters was done for the proposed i-SDPR with the existing techniques such as the DPR and SDPR approach to show the improvement. The results declare that the proposed techniques obtain better performance compared to the existing techniques.

在这项工作中，提出了一种智能多目标优化技术来优化各种参数，例如功耗（$P_C$), 计算时间 ($C_{吨}$) 和面积 ($\mathrm{一种}$) 用于使用安全动态部分重新配置 (SDPR) 架构设计的健康监控系统。具有成本效益的基于动态局部重配置 (DPR) 的现场可编程门阵列 (FPGA) 对于分析自动化和数据处理等许多应用非常有用。本文的新颖之处在于设计了一种新的智能 SDPR (i-SDPR) 模块，以便通过考虑各种性能参数在健康监测系统中实现更好的性能。SDPR 架构结合了两个可重构模块，例如 Encrypt 和 Authenticate，这导致核心功耗增加（$P_C$), 计算时间 ($C_{吨}$) 和面积 ($\mathrm{一种}$). 因此，为了提高基于 SDPR 的健康监测系统的性能，有必要优化性能参数，这是通过智能多目标进化 (MOEA) 技术实现的。为了更好地优化性能参数，考虑了智能多目标进化技术，例如 Niched-Pareto 遗传算法 (NPGA)、帕累托存档进化策略 (PAES) 和基于帕累托包络的选择算法 (PESA)。对于这项研究，采用了免费使用的 MIMIC-III 数据集，其中包含重症监护病房的各种重症患者数据。通过任意一个多目标进化算子对数据集进行处理，直到满足条件，然后转发到实现。$Ⓡ$V SX SoC 开发套件。对所提出的 i-SDPR 与现有技术（如 DPR 和 SDPR 方法）进行了各种性能参数的比较分析，以显示改进。结果表明，与现有技术相比，所提出的技术获得了更好的性能。