摘要
针对海上搜救资源调度决策困难、干扰多、实时性差、难以实现全局最优问题,本文以黄渤海海域为例,采用改进的非支配排序遗传(NSGA-Ⅱ)算法解决海上船舶搜救资源调度问题.首先,根据AIS以及北斗数据,建立了海上搜救资源的多目标优化模型;其次,改进的NSGA-Ⅱ算法采用基于正态分布交叉(NDX)算子,在扩大搜索范围的基础上,避免陷入局部最优,得到多目标问题完整的Pareto解集;采用综合评价法(TOPSIS)从Pareto解集中求得折衷解,即最终设计的搜救调度方案;最后,在考虑船舶数量约束以及时间约束的条件下,采用改进的NSGA-Ⅱ算法分别与NSGA-Ⅱ算法和贪婪算法进行对比,并采用黄渤海海域船舶采集数据进行仿真.结果表明该算法能够有效解决海上搜救资源调度优化问题.
To solve the problems of difficult decision-making,multiple interference factors,poor real-time performance and the realization of global optimization in maritime search and rescue(SAR)resource scheduling,this study employs an improved non-dominated sorting genetic(NSGA-II)algorithm by taking the Yellow Sea and the Bohai Sea as an example.Firstly,a multi-objective optimization model for maritime SAR resources is built based on AIS and BeiDou data.Secondly,the normal distribution crossover(NDX)-based operator is adopted by the improved NSGA-II algorithm to avoid falling into local optimum on the basis of expanding the search scope,and a complete Pareto solution set for the multi-objective problem is obtained.The comprehensive evaluation method(TOPSIS)is applied to obtain a compromise solution from the Pareto solution set,namely the optimal design of the search and rescue scheduling scheme.Finally,when the constraint factors such as the number of ships and time are considered,the improved NSGA-II algorithm is employed and compared with the NSGA-II and greedy algorithms.The simulations of the resource scheduling are carried out using the data collected from ships in the Yellow Sea and the Bohai Sea.The results show that the algorithm can effectively solve the problem of maritime SAR resource scheduling optimization.
作者
严梦迪
王海红
YAN Meng-Di;WANG Hai-Hong(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《计算机系统应用》
2023年第8期244-249,共6页
Computer Systems & Applications
基金
山东省自然科学基金重大基础研究项目(ZR2021ZD12)。
关键词
改进的非支配排序遗传
多目标优化模型
海上搜救
调度优化
全局最优
improved non-dominated sorting genetics
multi-objective optimization models
maritime search and rescue
scheduling optimization
global optimum
