基于非支配排序差异演化的应急资源多目标分配算法

苏兆品; 张国富; 蒋建国; 岳峰; 张婷

doi:10.16383/j.aas.2017.c160076

基于非支配排序差异演化的应急资源多目标分配算法

doi: 10.16383/j.aas.2017.c160076

苏兆品^1,2,,
张国富^1,2, ,,
蒋建国^1,2,,
岳峰^1,,
张婷^1,

1.
合肥工业大学计算机与信息学院合肥 230009
2.
合肥工业大学安全关键工业测控技术教育部工程研究中心合肥 230009

基金项目:

国家自然科学基金 61371155

安徽省科技攻关计划 1301b042023

安徽省自然科学基金 1608085MF131

安徽省自然科学基金 1508085MF132

国家自然科学基金 61573125

安徽省自然科学基金 1508085QF129

详细信息

作者简介:
苏兆品合肥工业大学计算机与信息学院副教授, IEEE会员.2008年获得合肥工业大学计算机科学与技术专业博士学位.主要研究方向为演化计算, 灾害应急决策, 多媒体安全.E-mail:szp@hfut.edu.cn

蒋建国合肥工业大学计算机与信息学院教授, 中国计算机学会高级会员.1989年获得合肥工业大学信号、电路与系统专业硕士学位.主要研究方向为分布式智能系统和数字图像处理与分析.E-mail:jgjiang@hfut.edu.cn

岳峰合肥工业大学科学技术研究院副研究员.2015年获得合肥工业大学计算机科学与技术专业博士学位.主要研究方向为软件工程和演化计算.E-mail:yuefeng@hfut.edu.cn

张婷合肥工业大学计算机与信息学院硕士研究生.2012年获得佛山科学技术学院计算机科学与技术专业学士学位.主要研究方向为灾害应急决策和演化计算.E-mail:tzhang@mail.hfut.edu.cn

通讯作者:
张国富合肥工业大学计算机与信息学院副教授, 中国自动化学会、IEEE会员.2008年获得合肥工业大学计算机科学与技术专业博士学位.主要研究方向为计算智能, 多Agent系统, 基于搜索的软件工程.本文通信作者.E-mail:zgf@hfut.edu.cn

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出版历程
- 收稿日期: 2016-01-22
- 录用日期: 2016-04-18
- 刊出日期: 2017-02-01

Multi-objective Approach to Emergency Resource Allocation Using None-dominated Sorting Based Differential Evolution

SU Zhao-Pin^{1,2
,},
ZHANG Guo-Fu^{1,2
, ,},
JIANG Jian-Guo^{1,2
,},
YUE Feng^1
,,
ZHANG Ting^1
,

1.
School of Computer and Information, Hefei University of Technology, Hefei 230009
2.
Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei University of Technology, Hefei 230009

Funds:

National Natural Science Foundation of China 61371155

Key Projects of Science and Technology of Anhui Province 1301b042023

Anhui Provincial Natural Science Foundation 1608085MF131

Anhui Provincial Natural Science Foundation 1508085MF132

National Natural Science Foundation of China 61573125

Anhui Provincial Natural Science Foundation 1508085QF129

More Information

Author Bio:
Associate professor at the School of Computer and Information, Hefei University of Technology. She is a member of IEEE. She received her Ph. D. degree in computer science and technology from Hefei University of Technology in 2008. Her research interest covers evolutionary computation, disaster emergency decision-making, and multimedia security

Professor at the School of Computer and Information, Hefei University of Technology. He is a senior member of China Computer Federation (CCF). He received his master degree in signals, circuits, and systems from Hefei University of Technology in 1989. His research interest covers distributed intelligent systems and digital image processing and analysis

Associate professor at the Institute of Science and Technology Management, Hefei University of Technology. He received his Ph. D. degree in computer science and technology from Hefei University of Technology in 2015. His research interest covers software engineering and evolutionary computation

Master student at the School of Computer and Information, Hefei University of Technology. She received her bachelor degree in computer science and technology from Foshan University in 2012. Her research interest covers disaster emergency decision-making and evolutionary computation

Corresponding author: ZHANG Guo-Fu Associate professor at the School of Computer and Information, Hefei University of Technology. He is a member of Chinese Association of Automation (CAA) and IEEE. He received his Ph. D. degree in computer science and technology from Hefei University of Technology in 2008. His research interest covers computational intelligence, multi-agent systems, and search-based software engineering. Corresponding author of this paper

摘要

摘要: 应急资源分配（Emergency resource allocation，ERA）是灾害应急管理中的核心环节，主要研究如何高效合理地把各储备点的应急救援物资分配给各发放点.然而，在大规模突发灾害发生后，每个发放点极可能会同时向多个储备点请求多种救援物资，从而带来潜在的应急资源冲突.为此，本文首先构建了考虑应急资源冲突消解的多储备点、多发放点、多种救援物资的应急资源多目标优化模型，并提出了一种基于非支配排序差异演化和编码修正机制的应急资源多目标分配算法.对比实验结果表明，该算法在大规模样本下能够从全局角度同时给出多个发放点的应急资源分配方案，有效实现多个储备点同时为多个发放点协同配备应急资源，而且不会产生任何应急资源冲突，为解决应急资源受限情况下的大规模应急资源分配问题提供了一个有益的尝试.
- 应急资源分配 /
- 多目标优化 /
- 差异演化 /
- 非支配排序 /
- 编码修正
Abstract: Emergency resource allocation (ERA) is a key topic in emergency management for sudden natural disasters, which mainly deals with how to reasonably and efficiently allocate the emergency relief supplies at reserve points to dispatch points. However, when an extraordinarily serious natural disaster occurs, each dispatch point may ask for many different emergency relief supplies at multiple reserve points at the same time, which will bring potential conflicts over emergency resources. To tackle this problem, a multi-objective optimization model is constructed considering multiple reserve points, multiple dispatch points, multiple emergency resources and emergency resource conflicts resolution. In addition, a multi-objective optimization algorithm for ERA is developed by using none-dominated sorting based differential evolution and encoding repair mechanism. Finally, comparative experimental results from large-scale samples show that our approach can deal with the ERA problem from an overall point of view, simultaneously give the allocation schemes of multiple reserve points for multiple dispatch points, realize different reserve points cooperate with each other on ERA for different dispatch points without any emergency resource conflict, which may provide a useful attempt to solve large-scale ERA problems under limited emergency resources.
- Emergency resource allocation (ERA) /
- multi-objective optimization /
- differential evolution /
- none-dominated sorting /
- encoding repair
注释:

1) 本文责任编委赵千川

HTML全文

图 1 应急联盟

Fig. 1 Emergency coalition

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图 2 DE算法流程图

Fig. 2 Flowchart of DE

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图 3 二维整数向量编码

Fig. 3 Two-dimensional integer vector encoding

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图 4 基于非支配排序的选择操作

Fig. 4 None-dominated sorting based selection

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图 5 ERNS-DE算法流程图

Fig. 5 Flowchart of ERNS-DE

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图 6 En 1中储备点和发放点的分布图

Fig. 6 Reserve and dispatch points in En 1

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图 7 En 2中储备点和需求点的分布图

Fig. 7 Reserve and dispatch points in En 2

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图 8 四种算法的运行时间

Fig. 8 Running time of four algorithms

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图 9 En 1环境中四种算法的Pareto最优解个数

Fig. 9 Numbers of Pareto solutions obtained by the four algorithms in En 1

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图 10 En 1环境中四种算法的Pareto最优解集

Fig. 10 Pareto solution sets obtained by the four algorithms in En 1

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图 11 En 2环境中四种算法的Pareto最优解个数

Fig. 11 Numbers of Pareto solutions obtained by the four algorithms in En 2

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图 12 En 2环境中四种算法的Pareto最优解集

Fig. 12 Pareto solution sets obtained by the four algorithms in En 2

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图 13 两种算法在每种环境中给出的应急联盟分布图

Fig. 13 Distribution chart of emergency coalitions

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表 1 ERNS-DE算法的参数设置

Table 1 Parameters setting for ERNS-DE

最大迭代次数	种群规模PS	缩放因子F	交叉概率CR
500	30	0.55	0.76

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表 2 两种算法在En 1环境中Pareto最优解

Table 2 Pareto solutions obtained by two algorithms in En 1

ERA参数情形	算法	Pareto最优解集(f₁, f₂)
Case 1	ERNS-DE	(162, 242), (181, 218), (185, 214), (171, 228), (164, 235), (187, 212), (169, 230), (173, 226), (154, 252), (180, 219), (189, 210), (175, 224), (155, 250), (168, 231), (165, 234), (194, 208), (183, 216), (184, 215), (170, 229), (167, 232), (179, 220), (166, 233), (178, 221), (163, 236), (186, 213), (176, 223), (172, 227), (182, 217), (157, 247), (177, 222)
Case 1	LPNO-HA	(155, 275)
Case 2	ERNS-DE	(194, 302), (205, 287), (193, 303), (209, 285), (199, 294), (191, 308), (192, 306), (201, 291), (187, 312), (189, 309), (195, 300)
Case 2	LPNO-HA	(196, 303)

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表 3 两种算法在En 2环境中Pareto最优解

Table 3 Pareto solutions obtained by two algorithms in En 2

ERA参数情形	算法	Pareto最优解集(f₁, f₂)
Case 1	ERNS-DE	(158, 252), (181, 218), (188, 211), (166, 233), (169, 230), (177, 222), (159, 246), (182, 217), (187, 212), (176, 223), (185, 214), (170, 229), (180, 219), (165, 235), (173, 226), (163, 239), (164, 237), (172, 227), (189, 210), (168, 231), (157, 253), (183, 216), (175, 224), (193, 209), (186, 213), (156, 254), (178, 221), (167, 232), (154, 259), (196, 208)
Case 1	LPNO-HA	(155, 283)
Case 2	ERNS-DE	(203, 274), (205, 271), (199, 276), (189, 289), (187, 293), (183, 313), (196, 278) (185, 306), (200, 275), (192, 286), (195, 281), (180, 320), (190, 288)
Case 2	LPNO-HA	(189, 293)

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