[1]娄艳秋[],庄毅[],顾晶晶[],等. 协同干扰环境下基于IMOABC的任务调度方法[J].计算机技术与发展,2017,27(11):46-51.
　LOU Yan-qiu[],ZHUANG Yi[],GU Jing-jing[],et al. A Task Scheduling Method Based on IMOABC in Collaboration Interference Environment[J].,2017,27(11):46-51.
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 协同干扰环境下基于IMOABC的任务调度方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
46-51
栏目:
智能、算法、系统工程
出版日期:
2017-11-10

文章信息/Info

Title:
 A Task Scheduling Method Based on IMOABC in Collaboration Interference Environment
文章编号:
1673-629X（2017)11-0046-06
作者:
 娄艳秋[1]; 庄毅[1]; 顾晶晶[1]; 霍瑛[2]
 1.南京航空航天大学 计算机科学与技术学院；2.南京工程学院 计算机工程学院
Author(s):
 LOU Yan-qiu[1]; ZHUANG Yi[1]; GU Jing-jing[1]; HUO Ying[2]
关键词:
 ; 协同干扰; 多目标优化; 任务调度; 人工蜂群算法
Keywords:
 ; collaboration interference; multi-objective optimization; task scheduling; artificial bee colony
分类号:
TP301
文献标志码:
A
摘要:
 
在协同干扰环境下,除了需要考虑最大限度地完成干扰任务,还需要最大程度地减少己方的损失消耗.在这种复杂的需求下,需要将协同干扰环境下的任务调度问题转化为多目标优化问题.针对如何最大限度地完成干扰任务,同时最大程度地减少无人作战飞行器(UCAV)的能量损失消耗问题,将干扰贡献值和损失消耗值作为目标函数,建立了基于多目标优化的协同干扰任务调度模型(MOTSM).提出了基于多目标优化的改进人工蜂群算法(IMOABC)的任务调度算法来求解该模型.IMOABC算法首先进行染色体的二进制编码,然后随机生成一个满足MOTSM模型约束条件的初始种群.对初始种群进行非支配快速排序以及拥挤度距离的计算,通过雇佣蜂、观察蜂、侦察蜂三种蜜蜂的配合,完成对最优解的搜索.通过仿真实验验证了该模型与算法的有效性.
Abstract:
 Not only the maximization of interference tasks but the minimization of energy loss itself is needed to be considered in the col-laborative interference environment. In this complex requirements,it is necessary to convert task scheduling into multi-objective optimiza-tion in the collaborative interference environment. Aiming at the problem of how to maximize the interference tasks and minimize the en-ergy loss of Unmanned Combat Aerial Vehicle ( UCAV) simultaneously,the Multi-Objective based Task Scheduling Model of collabora-tive interference ( MOTSM) is established which takes contribution value and loss consumption as the objective functions. A task schedu-ling algorithm based on Improved Multi-Objective Artificial Bee Colony ( IMOABC) is developed to solve the proposed model. First,it carries out the binary coding of chromosomes. Then an initial population that satisfies the MOTSM constraints is generated randomly,and performed in rapid non-dominated sorting and calculation of crowding distances. Through the cooperation of the three bees including the employed bees,onlookers and the scouts,the search for the optimal solution is finished. Finally,the effectiveness of the proposed model and algorithm is verified by simulation experiments.

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更新日期/Last Update: 2017-12-25