Time difference of arrival location method based on improved dung beetle optimizer algorithm

For Time Difference of Arrival (TDOA) location, an improved Dung Beetle Optimizer location algorithm based on golden sine strategy and self-adaptive Levy flight was proposed. In the population initialization stage, Bernoulli map was used to initialize the position of dung beetle population, which optimized the uneven distribution of population and limited search range. Then, the golden sinusoidal strategy is introduced into the dung beetle rolling behavior rules to enhance the local optimization ability and improve the convergence ability of the algorithm. Finally, self-adaptive Levy flight strategy was used to randomly update the location of the theft behavior of dung beetles, which decreased the constraint of the local extremum and improved the anti-jamming ability of the algorithm to the location noise. Experimental comparisons with Chan algorithm, Sparrow Search Algorithm(SSA), Whale Optimizer Algorithm(WOA), Grey Wolf Optimizer(GWO), and the Dung Beetle Optimizer demonstrate that the improved cockroach algorithm achieves higher positioning accuracy and convergence speed. It outperforms other algorithms and is suitable for TDOA location.