吴柏林2,
胡旺2,,,
康承旭1
1.湖南省地震局 长沙 410004
2.电子科技大学计算机科学与工程学院 成都 611731
基金项目:国家自然科学基金(61976046)，中国地震局地震科技星火计划(XH201801)
详细信息
作者简介:唐红亮：男，1979年生，高级工程师，硕士生导师，研究方向为地震业务计算机应用及信息化服务、地震大数据分析
吴柏林：男，1993年生，硕士生，研究方向为进化优化计算
胡旺：男，1974年生，副教授，硕士生导师，研究方向为进化优化计算及计算机应用技术
康承旭：男，1988年生，工程师，研究方向为地震应急及GIS应用、地震大数据分析
通讯作者:胡旺 huwang@uestc.edu.cn
中图分类号:TP399计量
文章访问数:2115
HTML全文浏览量:628
PDF下载量:76
被引次数:0
出版历程
收稿日期:2019-04-22
修回日期:2019-10-30
网络出版日期:2019-11-11
刊出日期:2020-03-19
Earthquake Emergency Resource Multiobjective Schedule Algorithm Based on Particle Swarm Optimization
Hongliang TANG1,Bolin WU2,
Wang HU2,,,
Chengxu KANG1
1. Hunan Earthquake Agency, Changsha 410004, China
2. School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 611731, China
Funds:The National Science Foundation of China(61976046), The Seism Science & Technology Spark Program of China Earthquake Administration (XH201801)
摘要
摘要:合理高效地优化调度救灾物资对提升地震应急救援效果具有重要意义。地震应急需要同时兼顾时效性、公平性和经济性等相互冲突的多个调度目标。该文对地震应急物资调度问题建立了带约束的3目标优化模型，并设计了基于进化状态评估的自适应多目标粒子群优化算法(AMOPSO/ESE)来求解Pareto最优解集。然后根据“先粗后精”的决策行为模式提出了由兴趣最优解集和邻域最优解集构成的Pareto前沿来辅助决策过程。仿真表明该算法能有效地获得优化调度方案，与其他算法相比，所得Pareto解集在收敛性和多样性上具有性能优势。
关键词:粒子群优化/
多目标优化/
地震应急/
物资调度
Abstract:It is of great significance to optimize emergency resource schedule for earthquake emergency rescue. The conflicting multiple schedule goals, such as time, fairness, and cost, should be taken into consideration together in an earthquake emergency resource schedule. A three-objective optimization model with constraints is constructed according to earthquake emergency resource schedule problems. An Adaptive MultiObjective Particle Swarm Optimization (PSO) based on Evolutionary State Evaluation (AMOPSO/ESE) is proposed to optimize this model for obtaining the Pareto optimal set. At the same time, based on the decision behavior pattern of "macro first and micro later", the two-level optimal solution sets consisting of an interest optimal solution set and their neighborhood optimal solution sets are proposed to represent the Pareto front roughly, which can simplify the decision-making process. The simulation results show that the multiobjective resource schedules can be effectively obtained by the AMOPSO/ESE algorithm, and the performance of the proposed algorithm is better than that of the chosen competed algorithms in terms of convergence and diversity.
Key words:Particle Swarm Optimization (PSO)/
Multiobjective optimization/
Earthquake emergency/
Resource schedule
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=b590e006-2a7a-4c8e-96f5-c4f2edf4719e
