The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

中文翻译：

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工业无线传感器网络中故障检测覆盖的混合利基免疫遗传算法

用于监控工业设备的工业无线传感器网络（IWSN）技术近年来受到越来越多的关注。IWSN中的传感器节点可以在随机部署条件下自发完成分布式组网，执行监测任务。因此，自组织IWSN特别适用于复杂环境下工业设备的故障检测和诊断。然而，由于单个传感器节点的检测能力有限，监测分布问题是典型的多维离散NP-hard组合随机优化问题，传统数学方法难以解决。为了提高目标监测能力，延长IWSN的生命周期，提出了一种用于优化故障检测目标覆盖模型的新型混合生态位免疫遗传算法（HNIGA）。它利用基因操作来进化抗体群，并应用小众技术来维持抗体群的多样性。因此，HNIGA 可以有效降低检测目标的失败率。为了验证HNIGA的性能，在不同的模拟条件下进行了一系列的模拟。具体而言，将HNIGA与遗传算法（GA）和模拟退火（SA）进行了比较。仿真结果表明，与其他两种算法相比，HNIGA 具有更快的收敛速度和更稳健的全局搜索能力。它利用基因操作来进化抗体群，并应用小众技术来维持抗体群的多样性。因此，HNIGA 可以有效降低检测目标的失败率。为了验证HNIGA的性能，在不同的模拟条件下进行了一系列的模拟。具体而言，将HNIGA与遗传算法（GA）和模拟退火（SA）进行了比较。仿真结果表明，与其他两种算法相比，HNIGA 具有更快的收敛速度和更稳健的全局搜索能力。它利用基因操作来进化抗体群，并应用小众技术来维持抗体群的多样性。因此，HNIGA 可以有效降低检测目标的失败率。为了验证HNIGA的性能，在不同的模拟条件下进行了一系列的模拟。具体而言，将HNIGA与遗传算法（GA）和模拟退火（SA）进行了比较。仿真结果表明，与其他两种算法相比，HNIGA 具有更快的收敛速度和更稳健的全局搜索能力。HNIGA 与遗传算法 (GA) 和模拟退火 (SA) 进行了比较。仿真结果表明，与其他两种算法相比，HNIGA 具有更快的收敛速度和更稳健的全局搜索能力。HNIGA 与遗传算法 (GA) 和模拟退火 (SA) 进行了比较。仿真结果表明，与其他两种算法相比，HNIGA 具有更快的收敛速度和更稳健的全局搜索能力。