Whale Optimization Algorithm for Efficient Task Allocation in the Internet of Things

In order to solve the problem of reducing worker costs and improving worker efficiency, the author proposes a whale optimization algorithm for efficient task allocation in the Internet of Things. This algorithm adopts the fuzzy chance constrained programming method to model the online time of workers, and introduces delay costs and idle costs based on whether there is delay or not, Due to the fact that the corresponding problem is a combinatorial optimization problem and belongs to the NP hard problem category, a two-stage task allocation algorithm is designed to solve it in combination with the whale optimization algorithm. The experimental results show that after being simulated by the algorithm, half of the workers reached the highest efficiency of 1, and the expected online time of the workers was less than 30, and the task execution time of the workers was less than 35. The task allocation algorithm designed by the author has higher worker efficiency compared to other algorithms and has broad application prospects