Variable speed multi-task allocation for mobile crowdsensing based on a multi-objective shuffled frog leaping algorithm

In multi-task studies of mobile crowdsensing, the possibility that a user may adopt another travel mode when completing the current task to the next is ignored. In addition, existing methods tend to allocate more tasks to the users with high reputation, which causes that few tasks will be assigned to new users with low reputation. In order to cover these shortages, a constrained multi-objective optimization model of variable speed multi-task allocation is established, which aims to maximize the user rewards and minimize the task completion time simultaneously. Meanwhile, the maximum number of fully paid tasks positively correlated with reputation is set for each user. To solve the constructed model, a three-stage multi-objective shuffled frog leaping algorithm is proposed, which introduces an objective anchored hybrid initialization operator based on heuristic information, a region mining strategy for the archive individuals, a discrete leaping rule to enhance the interaction of individual information and a constraint handling operator to reduce the loss of individual information. The performance of the proposed algorithm is evaluated by comparing it with five state-of-the-art algorithms on both real-world and synthetic instances. Experimental results show that the proposed algorithm can find a set of Pareto optimal allocation solutions with better convergence and distributions.