基于免疫猴群算法的传感器优化布置方法研究

Immune monkey algorithm for optimal sensor placement

将生物界中的免疫机制引入到猴群算法中,提出了一种用于传感器优化布置的免疫猴群算法。采用双重编码的方式,克服了原猴群算法只能解决连续变量优化问题的缺陷;采用混沌搜索的方式初始化猴群位置,以保证猴子能够均匀分布,提高了算法的全局搜索能力;通过在爬过程中引入深度爬的方式,增强了算法的局部搜索能力;在爬过程结束后加入基于浓度选择的机制对猴群进行初次选择,并对位置最优的猴子进行免疫克隆操作,以此保证猴群的多样性;在望过程结束后加入基于适应度的二次选择,并对位置较差的猴子进行免疫疫苗注射,以此提高算法的收敛能力。文末以大连世贸大厦为例,进行了参数敏感性分析以及传感器优化布置方案的选择,结果表明,免疫猴群算法的搜索效率较原猴群算法有了大幅提高,能较好地解决传感器优化布置问题。

The immune monkey algorithm （IMA） for optimal sensor placement （OSP） is proposed by introducing the immune mechanism of biosphere into the monkey algorithm （MA）. The dual-structure coding method is used to overcome that the original MA can only solve the optimization of continuous variables. Then, the chaotic search is adopted to initialize the monkey＇s location for ensuring the uniform distribution of monkey,which can improve the global search capability. Besides,the deep climb is introduced in the climb process to enhance the local search capability of the algorithm. After the end of the climb process,the first selection by density-dependent mechanism on the monkey is performed and the immune clone is done on the monkeys with best location to guarantee the diversity of monkey. While af- ter the end of the watch process, the second selection is added based on the fitness and the immune vacci- nation is carried out on the monkeys with poor location to improve the convergence of the algorithm. Finally, the parametric sensitivity analysis and OSP is done on the Dalian world trade building. The results show that the search efficiency of the IMA greatly increases compared with the original MA, which can better solve the OSP problem.