Task allocation method and system based on extreme learning machine cooperating with doliolaria glans group optimization algorithm

The invention belongs to the technical field of crowdsourcing task allocation, and particularly relates to a task allocation method and system of an extreme learning machine cooperating with a doliovessel swarm optimization algorithm, based on attributes of workers and tasks, an objective function is constructed, and a crowdsourcing task allocation scheme enabling the objective function to be maximized is obtained through three stages of task allocation in sequence, converting a multi-dimensional crowdsourcing task allocation optimization problem into a one-dimensional optimization problem, and obtaining a first-stage allocation result through a doliolaria group optimization algorithm; in the second-stage allocation, combining the dimensions with high correlation into a population, and updating the population through a doliolaria group optimization algorithm or a trained extreme learning machine to obtain a second-stage allocation result; in the third-stage allocation, the trained extreme learning machine or