Background:
In order to make up for the shortcomings of the traditional algorithm, Min-Min and
Max-Min algorithm are combined on the basis of the traditional genetic algorithm.
Methods:
In this paper, a new cloud computing task scheduling algorithm is proposed, which
introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task
completion time and load balancing as double fitness functions, which improves the quality of
initialization population, algorithm search ability and convergence speed.
Results:
The simulation results show that the algorithm is superior to the traditional genetic algorithm
and is an effective cloud computing task scheduling algorithm.
Conclusion:
Finally, this 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.
Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification
