To investigate the effectiveness of intelligent optimization algorithms in indoortime difference of arrival (TDOA) localization. Firstly, six intelligent optimization algorithms, namelywhite shark optimizer (WSO), chameleon swarm algorithm (CSA), snake optimizer (SO), whale optimization algorithm (WOA), grey wolf optimizer (GWO), sparrow search algorithm (SSA), were used for two-dimensional indoor TDOA localization. A comparative analysis of these algorithms was conducted in the indoor localization domain, and their performance was compared with the traditional Taylor algorithm in terms of localization error. Subsequently, the SOA algorithm was employed to optimize a back propagation (BP) neural network, and the optimized SOA-BP was used for localization. A comparison was made between the localization error of SOA-BP and the basic BP neural network. The results show that the six intelligent optimization algorithms use exhibit promising performance in indoor localization, with similar effects. The average localization error for each intelligent optimization algorithm is 0.44 m, representing an improvement of about 9.2% compared to the traditional Taylor algorithm. Furthermore, the localization error of SOA-BP is reduced by more than 30% when compared to the basic BP neural network.