Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing

Abstract

Aiming at the problems of high computing energy consumption and long time in traditional UAV-assisted edge computing research work, a computing resource allocation strategy using biological evolutionary algorithms in UAV-assisted mobile edge computing is proposed by introducing UAV swarms and genetic algorithms. Firstly, it analyzes the communication model for uplink transmission, the calculation model for local computing tasks, and UAV to perform computing tasks. Secondly, the objective function and overall model of system are constructed by comprehensively considering multiple constraints. Then, improved genetic algorithm is introduced into the model. On the basis of data encoding, crossover, mutation, and termination operations, the optimization performance of algorithm is greatly improved by multiple iterations of fitness function. Finally, the energy consumption of proposed algorithm and other two algorithms under the same number of iterations are compared and analyzed by simulation experiments. The experimental results show that the optimal solution, average, and variance of proposed algorithm for energy consumption are 52.354, 50.326, and 0.224, respectively, and its performance is better than other two comparison algorithms.