In this paper, a development of randomized and multi-agent algorithms is presented. The examples and their advantages are discussed. Different combined algo-rithms, which are applicable for the multi-sensor multi-target tracking problem are shown. These algorithms be-long to the class of methods used in derivative-free optimization and has proven efficacy in the problems includ-ing significant non-statistical uncertainties. The new al-gorithm, which is an Accelerated consensus-based SPSA algorithm is validated through the simulation.The main feature of that algorithm, combining the SPSA tech-niques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.