野草猴群算法的传感器优化布置方法研究

简易猴群算法存在初始化分布随机、爬步长固定、优秀猴子特征信息不能传承等缺陷，使算法求解性能受限。为解决以上问题，提出了一种用于传感器优化布置的野草猴群算法。该算法利用正态分布方法提高初始种群的多样性；采取自适应爬步长提升求解速度和搜索精度；融入野草繁殖进化和竞争排斥机制，扩大后代种群繁殖中优秀猴子的影响范围。以常用的模态置信度矩阵为传感器优化布置的目标函数，配置传感器的布置位置。以常用的8个测试函数和3个常用算法对其进行分析，验证了算法的可行性和有效性。最后以糊底机涂胶机构为例，进行传感器优化布置方案选择。实验结果表明，野草猴群算法的求解精度较简易猴群算法有大幅提高。

A weed monkey algorithm for optimal sensor placement

The simple monkey algorithm has the shortcomings of initial distribution randomization, fixed climb step length and incapability of inheriting the characteristics of excellent monkeys, which limits the solution performance of the algorithm. In order to solve the above problems, a weed monkey algorithm for optimal sensor placement is proposed. Normal distribution is used to enhance the diversity of initial monkey populations. Self adaptive climb step is introduced to improve the solution accuracy and convergence rate. Both weed reproduction evolution and competitive exclusion mechanism are used to enlarge the influence of excellent monkey on the monkey offspring population. The commonly used Modal Assurance Criterion (MAC) is used as the objective function of optimal sensor placement. The commonly used 8 test functions and 3 algorithms are used to verify the feasibility and effectiveness of the algorithm. Finally, the optimal sensor placement is carried out on the gelatinize mechanism of bag bottom pasting machine. The results show that, compared with the simple monkey algorithm, the solution accuracy of the weed monkey algorithm precision is greatly improved.