G.E. Weiqing* and Cui Yanru Pages 13 - 19 ( 7 )
Background: Min-min and max-min algorithms were combined on the basis of the traditional genetic algorithm to make up for its shortcomings.
Methods: In this paper, a new cloud computing task-scheduling algorithm that introduces min-min and max-min algorithms to generate initialization population, selects task completion time and load balancing as double fitness functions, and improves the quality of initialization population, algorithm searchability and convergence speed, was proposed.
Results: The simulation results proved that the cloud computing task-scheduling algorithm was superior to and more effective than the traditional genetic algorithm.
Conclusion: The paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.
Cloud computing, genetic algorithm, task scheduling, min-min algorithm, max-min algorithm, EIGA scheduling.
City College of Dongguan, University of Technology, Dongguan City, Guangdong, City College of Dongguan, University of Technology, Dongguan City, Guangdong