Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as dev...Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.展开更多
This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improv...This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and in- creases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clus- tering problem, which can be useful for the niches＇ forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters auto- matically and accurately.展开更多
为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中...为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中的任务进行聚类,基于聚类结果,在多个雾服务器之间使用改进的免疫粒子群优化算法进行任务调度。实验结果表明,该算法相比其它一些传统的调度算法在完工时间、成本、负载均衡方面都有一定提升。展开更多
近年来,建设清洁低碳安全高效的能源体系,发展可再生能源替代,构建以新能源为主体的新型电力系统成为我国能源发展的必然趋势。在风光资源富集地区,随着新能源装机不断增加,大型综合能源基地得到快速发展。该文基于主客观赋权法建立多...近年来,建设清洁低碳安全高效的能源体系,发展可再生能源替代,构建以新能源为主体的新型电力系统成为我国能源发展的必然趋势。在风光资源富集地区,随着新能源装机不断增加,大型综合能源基地得到快速发展。该文基于主客观赋权法建立多能互补综合能源基地评估体系,对我国“三北”、西南及东部沿海区域发展布局多能互补基地进行评估。为进一步提升多能互补基地经济效益,建立基于长短期记忆神经网络(long short term memory,LSTM)的电价预测模型及多能互补日前优化调度模型,利用粒子群优化算法进行寻优,以实现能源基地综合收益最大化的日前优化调度目标。最后,以甘肃陇东千万kW级多能互补综合能源基地为例,分别开展夏季及冬季典型日的优化调度算例仿真,结果表明,该优化调度方法能够促进基地内新能源消纳的同时最大化能源基地综合收益,为大型综合能源基地的日前优化调度提供技术支撑。展开更多
文摘Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.
基金supported by the National Natural Science Foundation of China (708710157103100271171030)
文摘This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and in- creases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clus- tering problem, which can be useful for the niches＇ forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters auto- matically and accurately.
文摘为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中的任务进行聚类,基于聚类结果,在多个雾服务器之间使用改进的免疫粒子群优化算法进行任务调度。实验结果表明,该算法相比其它一些传统的调度算法在完工时间、成本、负载均衡方面都有一定提升。
文摘近年来,建设清洁低碳安全高效的能源体系,发展可再生能源替代,构建以新能源为主体的新型电力系统成为我国能源发展的必然趋势。在风光资源富集地区,随着新能源装机不断增加,大型综合能源基地得到快速发展。该文基于主客观赋权法建立多能互补综合能源基地评估体系,对我国“三北”、西南及东部沿海区域发展布局多能互补基地进行评估。为进一步提升多能互补基地经济效益,建立基于长短期记忆神经网络(long short term memory,LSTM)的电价预测模型及多能互补日前优化调度模型,利用粒子群优化算法进行寻优,以实现能源基地综合收益最大化的日前优化调度目标。最后,以甘肃陇东千万kW级多能互补综合能源基地为例,分别开展夏季及冬季典型日的优化调度算例仿真,结果表明,该优化调度方法能够促进基地内新能源消纳的同时最大化能源基地综合收益,为大型综合能源基地的日前优化调度提供技术支撑。
