基于交叉熵金鹰优化算法的室内可见光定位

Indoor Visible Light Positioning Based on Cross-entropy Golden Eagle Optimization Algorithm

为改善金鹰算法在求解室内可见光定位问题时的优化能力,基于重要度抽样技术和Kullback-Leibler距离的交叉熵(CE)方法,提出1种新的交叉熵金鹰优化(CEGEO)算法。将交叉熵方法融合到金鹰优化(GEO)算法中,通过协同演化获得的新种群更新抽样概率分布参数,加速算法的迭代进程,降低抽样样本数和计算成本,提高算法的全局优化能力;同时,采用共同更新得到更好的个体,大幅度增加种群的多样性,改善算法的收敛速度。采用CEGEO算法及其他4种算法对标准测试函数和室内可见光定位优化问题进行仿真实验,比较分析CEGEO算法的性能。结果表明:与其他4种算法相比,提出的CEGEO算法具有更高的求解精度和更快的收敛速度,能有效平衡全局和局部搜索能力,寻优能力更强;在室内可见光定位中定位精度小于2.74E-12cm,估计位置误差最小,平均误差接近2.07E-15cm,CEGEO算法是1种解决室内可见光定位优化问题的有效算法。

To improve the optimization ability of the golden eagle optimization(GEO)algorithm in solving indoor visible light positioning problems,a new cross-entropy golden(CE)eagle optimization(CEGEO)algorithm was proposed based on the importance sampling technique and the Kullback-Leibler distance CE method.Integrating the cross entropy method into the golden eagle optimization(GEO)algorithm,the sampling probability distribution parameters were updated by a new population obtained through collaborative evolution to accelerate the iterative process of the algorithm,and reduce the number of sampling samples and computational costs,and the global optimization capability of the algorithm was improved.Meanwhile,better individuals were obtained through common updates to significantly increase the diversity of the population,and improve the convergence speed of the algorithm.Simulation experiments on standard test functions and indoor visible light positioning optimization problems were conducted using CEGEO algorithm and four other algorithms to compare and analyze the performance of CEGEO algorithm.The results show that compared with the other four algorithms,the proposed CEGEO algorithm has higher solving accuracy and faster convergence speed,can effectively balance global and local search capabilities,and has stronger optimization ability.In indoor visible light positioning,the positioning accuracy is less than 2.74E-12 cm,the estimated position error is the smallest,and the average error is close to 2.07E-15 cm,which is an effective algorithm for solving the optimization problem of indoor visible light positioning.