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| 1 | Reentry trajectory rapid optimization for hypersonic vehicle satisfying waypoint and no-fly zone constraints显示文摘To rapidly generate a reentry trajectory for hypersonic vehicle satisfying waypoint and no-fly zone constraints, a novel optimization method, which combines the improved particle swarm optimization(PSO) algorithm with the improved Gauss pseudospectral method(GPM), is proposed. The improved PSO algorithm is used to generate a good initial value in a short time,and the mission of the improved GPM is to find the final solution with a high precision. In the improved PSO algorithm, by controlling the entropy of the swarm in each dimension, the typical PSO algorithm's weakness of being easy to fall into a local optimum can be overcome. In the improved GPM, two kinds of breaks are introduced to divide the trajectory into multiple segments, and the distribution of the Legendre-Gauss(LG) nodes can be altered, so that all the constraints can be satisfied strictly. Thereby the advantages of both the intelligent optimization algorithm and the direct method are combined. Simulation results demonstrate that the proposed method is insensitive to initial values, and it has more rapid convergence and higher precision than traditional ones. | Lu Wang Qinghua Xing Yifan Mao | 2015 | Journal of Systems Engineering and Electronics2015，26，6： | 5 |
| 2 | Resource allocation optimization of equipment development task based on MOPSO algorithm显示文摘Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively. | ZHANG Xilin TAN Yuejin and YANG Zhiwei | 2019 | Journal of Systems Engineering and Electronics2019，30，6： | 4 |
| 3 | 公共订单服务平台下制造联盟生产与运输两阶段协同调度成本最小化问题研究显示文摘云制造模式下为了实现社会制造资源的高效连接,满足不同客户的制造服务需求,制造企业联盟需要建立共享制造资源的公共服务平台,实现制造资源与服务的开放协作、社会资源的高度共享.文章研究了公共服务平台下面向订单生产的制造联盟生产与运输两阶段协同优化问题,同时引入恶化工件概念,首先证明该问题可以简化为经典平行机调度问题,即是NP难问题,然后分析每个制造商处加工订单集合已知情形下最优排序的结构性质,提出改进的列表调度启发式算法,并通过仿真实验说明该算法能够有效整合制造资源、分配制造订单、协同生产与运输. | 刘捷先 | 2018 | 湖北文理学院学报2018，39，8： | 0 |
| 4 | 云计算任务调度的混合遗传蚁群算法研究显示文摘针对云计算任务调度效率与负载均衡问题,提出一种基于混合蚁群遗传算法的任务调度方法。该算法通过分析云计算任务调度特点,以最小任务调度完成时间为优化目标,通过任务分组、重新设计变异算子和信息素挥发系数,实现任务到资源调度的完成时间最小。通过CloudSim平台仿真,并与Max-Min、Min-Min、GA和AIGA算法对比,结果表明,所提出的算法有效缩短了任务调度的完成时间,降低了运营成本,具有优越的综合性能。 | 任小强 王浩宇 林慧琼 赵超 | 2023 | 唐山师范学院学报2023，45，3： | 0 |
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