针对现有的船舶集中式控制系统无法高效地处理迅速增长的电子设备与智能化任务的问题，研究了边缘计算架构与基于多目标优化的任务调度问题。提出了一种基于边缘计算的智能船舶的分布式控制架构，针对该架构中边缘计算层任务多且计算量大带来的调度优化问题，基于非支配排序遗传算法Ⅲ（non-dominated sorting genetic algorithm Ⅲ, NSGA-Ⅲ）通过改进并设计种群编码、初始化种群策略、离散的自适应交叉策略与离散的自适应变异策略，提升了算法在计算任务调度问题上的综合性能。仿真实验表明，相较于其他算法，所设计的NSGA-Ⅲ-ADO算法的解集在总计算时延、总计算能耗以及负载均衡度上的平均性能至少提升了6.8%、5.9%以及0.8%，同时其收敛速度也更快，能够更好地解决计算任务调度问题。

In view of the problem that the existing ship centralized control system can not efficiently handle the rapid growth of electronic equipment and intelligent tasks, the edge computing architecture and task scheduling based on multi-objective optimization were studied. A distributed control architecture of intelligent ship based on edge computing was proposed. Aiming at the scheduling optimization problem caused by many tasks and large amount of computation in the edge computing layer in the architecture, the comprehensive performance of the algorithm in computing task scheduling problem was improved and designed based on non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ) by improving and designing population coding, initialization population strategy, discrete adaptive crossover strategy and discrete adaptive mutation strategy. The simulation experiments show that compared with other algorithms, the average performance of the solution set of the NSGA-Ⅲ-ADO algorithm designed is improved by at least 6.8%, 5.9% and 0.8% in total computing delay, total computing energy consumption and load balancing, and its convergence speed is faster, which can better solve the problem of computing task scheduling.