基于差分进化算法多智能体任务分配  

Multi-agent task assignment based on differential evolution algorithm

作　　者：熊远武 赵岭忠 翟仲毅[1,2] XIONG Yuan-wu;ZHAO Ling-zhong;ZHAI Zhong-yi(School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学计算机与信息安全学院,广西桂林541004 [2]桂林电子科技大学广西可信软件重点实验室,广西桂林541004

出　　处：《计算机工程与设计》2019年第10期3020-3029,共10页Computer Engineering and Design

基　　金：国家自然科学基金项目(61562015);广西自然科学基金项目(2015GXNSFAA139307、2015GXNSFDA139038);广西可信软件重点实验室基金项目(kx201505)

摘　　要：为提升多智能体系统的工作效率,克服差分进化算法DE (differential evolution)在多智能体系统任务分配过程中的不足,提出一种基于QOC (quantization orthogonal crosser)策略改进的正交交叉差分进化算法AOCDE (an orthogonal crosser differential evolution)。QOC在DE算法基础上,引入正交思想和数据量化技术,通过改变交叉策略,增强算法全局寻优能力,提升算法收敛性。使用该算法解决多智能体系统任务分配问题,将仿真结果与其它算法进行比较,验证了该算法的有效性。To improve the efficiency of multi-agent system and overcome the shortcoming of DE(difference evolution)in task assignment of multi-agent system,an improved AOCDE algorithm(an orthogonal crosser difference evolution)was proposed based on QOC(quantizing orthogonal crosser)strategy.QOC based on DE algorithm,orthogonal idea and data quantization technology were introduced,through changing crossover strategy,the algorithm’s global optimization ability was enhanced,and the convergence of the algorithm was improved.AOCDE algorithm was used to solve the task assignment problem of multi-agent system.The simulation results were compared with other algorithms to verify the effectiveness of the proposed algorithm.

关 键 词：任务分配 智能体系统 正交矩阵 正交交叉 数据量化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]