Cloud Computing Task Scheduling Based on Multi-strategy Improved Harris Hawks Optimization

Aiming at the problem of multi-objective optimization conflict between makespan and average resource utilization in cloud computing task scheduling, this paper proposed a cloud computing task scheduling strategy based on multi-strategy improved harris hawk optimization algorithm. Firstly, the fusion strategy of Tent chaotic sequence and opposition-based learning is introduced to initialize the population to enhance the diversity of the population and the quality of the initial solution, thereby improving the optimization efficiency of the population; Secondly, an energy adaptive exponential decay factor is employed as a control parameter for global exploration and local exploitation to improve the accuracy and convergence speed of the algorithm; Finally, apply the elite representative exploration mechanism of the grey wolf optimization (GWO) algorithm to the global search stage of the population to improve the global search ability of the algorithm and avoid falling into a local optimal state. The simulation results show that for task scheduling with different distributions and scales, the proposed algorithm is superior to the comparison algorithm in terms of reducing makespan and increasing the average resource utilization rate, and has obvious advantages in algorithm convergence and convergence accuracy.