Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

Abstract

This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user's preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.