An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.     

城市供水管网抗震功能可靠度分析

介绍了供水系统功能分析中的经验渗漏模型,在此基础上给出了一个新的改进算法。将建议模型与供水系统水力分析方法相结合,并引入一般结构分析中的一次二阶矩方法,提出了一类求解供水网络节点及系统功能可靠度的方法。对一典型供水管网进行了流分析,将建议模型计算所得结果与常规渗漏模型进行了比较,结果说明建议模型可行,计算结果合理可信。