Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm

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.

Automated summary

This paper proposes a cloud task scheduling method based on particle swarm optimization genetic hybrid algorithm. By using phagocytosis mechanism and crossover mutation of genetic algorithm to change the position of particles, the search range of the solution space is expanded. Experimental results show that the proposed algorithm significantly improves the completion time of cloud tasks and has high convergence accuracy, demonstrating the effectiveness of the algorithm in cloud task scheduling.