A NSGA-II Algorithm for Task Scheduling in UAV-Enabled MEC System

Abstract

In this paper, we investigate the task scheduling problem in the UAV-enable Mobile Edge-Computing (MEC) system with the objectives of minimizing the cost and the completion time. A NSGA-II algorithm is proposed for the problem under study. The solution is represented as a two-dimension location sequence. Major components of NSGA-II are delicately designed including the feasible solution generation method (FSGM) and genetic operations of crossover, mutation and selection. Three strategies are introduced in FSGM. A simulated annealing local search is integrated into the crossover operation, and meanwhile two novel mutation methods are proposed. The Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal is more effective and robust than the three existing algorithms.