基于Cell-DEVS的森林灭火资源调度狼群优化算法

doi:10.11772/j.issn.1001-9081.2017102603

计算机应用 ›› 2018, Vol. 38 ›› Issue (5): 1494-1499.DOI: 10.11772/j.issn.1001-9081.2017102603

• 应用前沿、交叉与综合 • 上一篇下一篇

基于Cell-DEVS的森林灭火资源调度狼群优化算法

李斌¹, 陈爱斌¹, 周国雄¹, 周涛²

1. 中南林业科技大学计算机与信息工程学院, 长沙 410004;
2. 湖南省林业厅森林消防航空护林站, 长沙 410007

收稿日期:2017-11-02 修回日期:2017-12-13 出版日期:2018-05-10 发布日期:2018-05-24
通讯作者: 周国雄
作者简介:李斌(1992-),男,河南三门峡人,硕士研究生,主要研究方向:林业信息智能化决策、GIS应用开发;陈爱斌(1971-),男,湖南株洲人,教授,博士,CCF会员,主要研究方向:图像处理、模式识别、智能系统;周国雄(1980-),男,湖南郴州人,副教授,博士,主要研究方向:图像处理;周涛(1986-),男,湖南浏阳人,高级工程师,硕士,主要研究方向:信息系统管理、软件工程。
基金资助:
国家林业局引进国际先进林业科学技术项目（2014-4-09）；湖南省研究生科技创新基金资助项目（CX2017B407）；中南林业科技大学研究生科技创新基金资助项目（CX2017B16）。

Wolves optimization algorithm based on Cell-DEVS for forest fire-fighting resource scheduling

LI Bin¹, CHEN Aibin¹, ZHOU Guoxiong¹, ZHOU Tao²

1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha Hunan 410004, China;
2. Forest Fire Aviation Forest Guard Station, Forestry Department of Hunan Province, Changsha Hunan 410007, China

Received:2017-11-02 Revised:2017-12-13 Online:2018-05-10 Published:2018-05-24
Contact: 周国雄
Supported by:
This work is partially supported by State Forestry Administration Introduced International Advanced Forestry Science and Technology Project of China (2014-4-09), the Hunan Provincial Innovation Foundation for Postgraduates (CX2017B407), the Scientific Innovation Fund for Postgraduates of Central South University of Forestry and Technology (CX2017B16).

摘要/Abstract

摘要： 针对森林消防调度兵力组织难、精细化程度不高的问题，提出了一种基于Cell-DEVS的森林灭火资源调度狼群优化算法。首先，利用Rothermel林火蔓延模型，对林火蔓延速度进行等级划分；其次，根据森林消防"控制要害"的原则，对森林灭火资源调度进行耦合驱动模块化建模，建立了基于Cell-DEVS模型的狼群强者生存更新机制（WSSUM）；最后，针对森林灭火资源在单位时间步长局部调度搜索中不够精细的问题，提出了一种改进局部搜索策略狼群优化算法（WOA），对资源在局部游走阶段实现调度交互。与WSSUM算法相比，WOA在减少任务执行时间的同时，改善了局部搜索性能，实验结果表明：收敛速度较改进前提高了10.1%。该研究适应于配备了定位设备的单兵消防作战指挥系统，实现差异化精细调兵。

关键词: Cell-DEVS, 耦合模型, 资源调度, 局部搜索, 狼群优化算法

Abstract: In view of the difficulty of forest fire-fighting dispatching force organization and low degree of refinement,a wolves optimization algorithm based on Cell-DEVS(Discrete Event System Specification) for forest fire-fighting resource scheduling was proposed. Firstly, Rothermel forest fire spread model was used to classify forest fire spread rate. Secondly, according to the principle of control the key in forest fire control, the forest fire-fighting resource scheduling model was built by a way of coupling drive modular designing,and a Wolves Strong Survival Update Mechanism (WSSUM) based on the Cell-DEVS model was proposed. Finally, aiming at the problem that the scheduling of forest fire-fighting resources was not fine enough in the local search of unit time step, an modified Wolves Optimization Algorithm (WOA) based on improved local search strategy was adopted to schedule the forest fire fighting resources in the local walk interactively. In the comparison experiments with WSSUM algorithm, the WOA improved the local search performance while reducing the task execution time. The experimental results show that the convergence speed is improved by 10.1% compared with that before improvement. The study adapts to the individual fire-fighting command system equipped with locating equipment to realize differentiated fine force dispatching.

Key words: Cell-DEVS, coupled model, resource scheduling, local search, wolves optimization algorithm

中图分类号:

TP391.9

李斌, 陈爱斌, 周国雄, 周涛. 基于Cell-DEVS的森林灭火资源调度狼群优化算法[J]. 计算机应用, 2018, 38(5): 1494-1499.

LI Bin, CHEN Aibin, ZHOU Guoxiong, ZHOU Tao. Wolves optimization algorithm based on Cell-DEVS for forest fire-fighting resource scheduling[J]. Journal of Computer Applications, 2018, 38(5): 1494-1499.

参考文献

[1] REN Y, TIAN G. Emergency scheduling for forest fires subject to limited rescue team resources and priority disaster areas[J]. IEEJ Transactions on Electrical & Electronic Engineering, 2016, 11(6):753-759.
[2] TIAN G, REN Y, ZHOU M C. Dual-objective scheduling of rescue vehicles to distinguish forest fires via differential evolution and particle swarm optimization combined algorithm[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(11):3009-3021.
[3] 张忠平, 冯玉鹏, 张雪楠. 基于标准差及二次分配的启发式网格资源调度算法[J]. 小型微型计算机系统, 2016, 37(2):259-263.(ZHANG Z P,FENG Y P,ZHANG X N. Heuristic grid resource scheduling algorithm based on standard deviation and quadratic allocation[J].Journal of Chinese Computer Systems, 2016,37(2):259-263.)
[4] HU X, SUN Y, NTAIMO L. DEVS-FIRE:design and application of formal discrete event wildfire spread and suppression models[J]. Simulation:Transactions of the Society for Modeling & Simulation International, 2012, 88(3):259-279.
[5] HU X, NTAIMO L. Integrated simulation and optimization for wildfire containment[J]. ACM Transactions on Modeling & Computer Simulation, 2009, 19(4):1-29.
[6] KOMAKI G M, KAYVANFAR V. Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time[J]. Journal of Computational Science, 2015, 8:109-120.
[7] LU C, GAO L, LI X, et al. A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry[J]. Engineering Applications of Artificial Intelligence, 2017, 57(C):61-79.
[8] 李国亮, 魏振华, 徐蕾. 基于改进搜索策略的狼群算法[J]. 计算机应用, 2015, 35(6):1633-1636.(LI G L,WEI Z H,XU L. Wolves algorithm based on improved search strategy[J].Journal of Computer Applications, 2015, 35(6):1633-1636.)
[9] AL-HABASHNA A, WAINER G. Modeling pedestrian behavior with Cell-DEVS:theory and applications[J]. Simulation:Transactions of the Society for Modeling & Simulation International, 2015, 92(2):117-139.
[10] 胡睿, 张国春, 柳少军. 基于DEVS的防空作战模型可重用性研究[J]. 系统仿真学报, 2011, 23(S1):30-33.(HU R, ZHANG G C, LIU S J. DEVS-based model reuse framework of anti-air defense operations[J].Journal of System Simulation, 2011, 23(S1):30-33.)
[11] 胡建鹏, 黄林鹏. 基于P-DEVS的可执行体系结构建模与仿真方法[J]. 系统仿真学报, 2016, 28(2):283-291.(HU J P,HUANG L P. Modeling and simulation of executable architecture based on P-DEVS[J]. Journal of System Simulation, 2016, 28(2):283-291.)
[12] 周强, 周永权. 一种基于领导者策略的狼群搜索算法[J]. 计算机应用研究, 2013, 30(9):2629-2632.(ZHOU Q, ZHOU Y Q. Wolf colony search algorithm based on leader strategy[J]. Application Research of Computers, 2013, 30(9):2629-2632.)
[13] 单延龙, 舒立福, 王洪伟,等. Rothermel火蔓延模型特征参数的解析[J]. 森林防火, 2003(1):22-25.(SHAN Y L, SHU L F, WANG H W, et al. Analysis of characteristic parameters of Rothermel fire spread model[J].Forest Fire Prevention, 2003(1):22-25.)
[14] 王冰. 基于局部最优解的改进人工蜂群算法[J]. 计算机应用研究, 2014,31(4):1023-1026.(WANG B. Improved artificial bee colony algorithm based on local best solution[J]. Application Research of Computers, 2014,31(4):1023-1026.)
[15] 刘三阳, 张平, 朱明敏. 基于局部搜索的人工蜂群算法[J]. 控制与决策, 2014, 29(1):123-128.(LIU S Y, ZHANG P, ZHU M M. Artificial bee colony algorithm based on local search[J]. Control and Decision, 2014, 29(1):123-128.)
[16] 黎夏, 叶嘉安. 遗传算法和GIS结合进行空间优化决策[J]. 地理学报, 2004, 59(5):745-753.(LI X, YE J A. Optimal spatial search using genetic algorithms and GIS[J]. Acta Geographica Sinica, 2004, 59(5):745-753.)
[17] 陈军, 赵仁亮, 乔朝飞. 基于Voronoi图的GIS空间分析研究[J]. 武汉大学学报(信息科学版), 2003,28(S1):37-42.(CHEN J,ZHAO R L,QIAO C F. Voronoi diagram-based GIS spatial analysis[J]. Geomatics and Information Science of Wuhan University, 2003, 28(S1):37-42.)
[18] 杜萌, 赵冬玲, 杨建宇, 等. 基于元胞自动机复合模型的土地利用演化模拟——以北京市海淀区为例[J]. 测绘学报, 2015, 44(b12):68-74.(DU M, ZHAO D L,YANG J Y,et al.Simulation of land use change in Haidian district of Beijing city based on CA compound model[J].Acta Geodaetica et Cartographica Sinica, 2015, 44(b12):68-74.)

基于Cell-DEVS的森林灭火资源调度狼群优化算法

Wolves optimization algorithm based on Cell-DEVS for forest fire-fighting resource scheduling

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