Optimization Resource Allocation in NOMA-based Fog Computing with a Hybrid Algorithm

Abstract

In this paper, a new hybrid algorithm based on the strengths of gray wolf and genetic algorithms is proposed to solve the problem of heterogeneous resource allocation in a fog environment with NOMA. The purpose of this algorithm is to prevent getting stuck in local optimization and reduce energy consumption and latency. The strength of the Gray Wolf algorithm is the use of multiple guides and the speed of convergence. The genetic algorithm avoids getting caught up in local optimization with a comprehensive search. In the proposed method, the strengths of both algorithms are used. Combining these two algorithms makes it possible to explore by updating solutions using the mutation and crossover of the genetic algorithm in the gray wolf algorithm. The results show that compared to the standard gray wolf and genetic algorithm, the proposed method can reduce latency and energy consumption. On the other hand, in the convergence discussion, the proposed algorithm, while maintaining the execution speed, has better convergence than the two mentioned algorithms and is not caught in the local optimization.