Task scheduling in cloud environment is a hot topic in current research. Effective scheduling of massive tasks submitted by users in cloud environment is of great practical significance for increasing the core competitiveness of companies and enterprises and improving their economic benefits. Faced with the urgent need for an efficient scheduling strategy in the real world, this paper analyzed the process of cloud task scheduling, and proposed a particle swarm optimization genetic hybrid algorithm based on phagocytosis PSO_PGA. Firstly, each generation of particle swarm is divided, and the position of the particles in the sub population is changed by using phagocytosis mechanism and crossover mutation of genetic algorithm, so as to expand the search range of the solution space. Then the sub populations are merged, which ensures the diversity of particles in the population and reduces the probability of the algorithm falling into the local optimal solution. Finally, the feedback mechanism is used to feed back the flight experience of the particle itself and the flight experience of the companion to the next generation particle population, so as to ensure that the particle population can always move towards the direction of excellent solution. Through simulation experiments, the proposed algorithm is compared with several other existing algorithms, and the results show that the proposed algorithm significantly improves the overall completion time of cloud tasks, and has higher convergence accuracy. It shows the effectiveness of the algorithm in cloud task scheduling.

云环境下的任务调度是当前研究的热点。有效地调度用户在云环境中提交的海量任务，对于提高企业和企业的核心竞争力，提高企业的经济效益具有重要的现实意义。面对现实世界中迫切需要有效的调度策略的需求，本文分析了云任务调度的过程，提出了一种基于吞噬作用PSO_PGA的粒子群优化遗传混合算法。首先，对每一代粒子群进行划分，利用吞噬作用机制和遗传算法的交叉突变改变子种群中粒子的位置，从而扩大了求解空间的搜索范围。然后合并子种群，这样可以确保总体中粒子的多样性，并降低算法陷入局部最优解的可能性。最后，使用反馈机制将粒子本身的飞行经验和同伴的飞行经验反馈给下一代粒子群，以确保粒子群始终可以朝着出色解的方向移动。通过仿真实验，将该算法与现有的几种其他算法进行比较，结果表明，该算法显着提高了云任务的整体完成时间，具有较高的收敛精度。它显示了该算法在云任务调度中的有效性。