An Improved Cuckoo Search Algorithm Based on Elite Opposition-based Learning for Indoor Visible Light Positioning

An Improved Cuckoo Search Algorithm Based on Elite Opposition-based Learning for Indoor Visible Light Positioning

In the cuckoo search algorithm, the structure is simple, and the parameters are not much, but it is easy to trap into the local optimum, and in the later period, the convergence speed is plodding. Aiming at the shortcomings of the standard cuckoo algorithm, a modified cuckoo algorithm (EACSDAM) is presented in this paper, which adopts elite reverse learning to enhance the population diversity, and increases the step factor and discovery probability to improve the global detection and local searchability. Eight standard test functions are used to simulate the EACSDAM algorithm. Compared with the standard cuckoo algorithm and the other two improved algorithms, the accuracy and convergence speed of EACSDAM are greatly improved. In the end, EACSDAM is used to optimize the indoor 3D visible light positioning. The simulation results indicate that EACSDAM has a more powerful ability for global optimization, and more accurate positioning, and the positioning error is significantly reduced.