高速三体船研究综述

从水动力理论、模型试验和船型优化等方面对高速三体船的研究进展进行了综述,阐述了高速三体船的应用前景,归纳评述了有关的研究成果和尚存在的问题,并提出了关于这种新船型的若干研究方向.     

自航式鱼雷发射装置发射管长度优化研究

基于某型鱼雷自航发射的内弹道数学模型,选择自航管的总长度作为优化目标,讨论了约束函数的取值,确定了优化问题的数学表达.采用惩罚函数法进行寻优计算,针对约束函数具有不可微和非线性的特点,采用模式搜索算法进行了无约束优化的求解,给出了优化结果并进行了比较.     

Approximating nondominated sets in continuous multiobjective optimization problems

Many important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or ε‐constrained problems, or discretizations‐based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation‐based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A generalization of the weighted set covering problem

We study a generalization of the weighted set covering problem where every element needs to be covered multiple times. When no set contains more than two elements, we can solve the problem in polynomial time by solving a corresponding weighted perfect b‐matching problem. In general, we may use a polynomial‐time greedy heuristic similar to the one for the classical weighted set covering problem studied by D.S. Johnson [Approximation algorithms for combinatorial problems, J Comput Syst Sci 9 (1974), 256–278], L. Lovasz [On the ratio of optimal integral and fractional covers, Discrete Math 13 (1975), 383–390], and V. Chvatal [A greedy heuristic for the set‐covering problem, Math Oper Res 4(3) (1979), 233–235] to get an approximate solution for the problem. We find a worst‐case bound for the heuristic similar to that for the classical problem. In addition, we introduce a general type of probability distribution for the population of the problem instances and prove that the greedy heuristic is asymptotically optimal for instances drawn from such a distribution. We also conduct computational studies to compare solutions resulting from running the heuristic and from running the commercial integer programming solver CPLEX on problem instances drawn from a more specific type of distribution. The results clearly exemplify benefits of using the greedy heuristic when problem instances are large. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Optimal power control in a wireless network using a model with stochastic link coefficients

This paper addresses optimal power allocation in a wireless communication network under uncertainty. The paper introduces a framework for optimal transmit power allocation in a wireless network where both the useful and interference coefficients are random. The new approach to power control is based on a stochastic programming formulation with probabilistic SIR constraints. This allows to state the power allocation problem as a convex optimization problem assuming normally or log‐normally distributed communication link coefficients. Numerical examples illustrate the performance of the optimal stochastic power allocation. A distributed algorithm for the decentralized solution of the stochastic power allocation problem is discussed. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

型号项目工期风险管理决策模型与自适应遗传算法

针对型号项目的过程特点,建立了型号项目工期风险的管理决策模型。模型考虑了型号项目中活动重叠、活动迭代、活动执行时间的不确定性和可更新资源总量等重要的工期风险影响因素。从模型的特点出发,给出了问题求解的基于自适应遗传算法的仿真优化方法。算例显示,该算法能较好地求解本文的工期风险管理决策问题。     

防空多传感器网络的拓扑优化模型

防空多传感器网络是防空网络化作战体系结构的重要组成部分。针对防空多传感器网络的网络结构模型,研究了已知网络中单元节点的位置和数量,如何最优确定网络中继器的位置、数量及其与单元节点的连接关系,即网络拓扑优化设计问题。最后给出了防空多传感器网络拓扑优化设计的数学模型及其求解方法。     

弱拓扑下集值最优化问题的通有稳定性

用函数的上图象之间的Hausdoff距离定义最优化问题目标函数间的距离。在此弱拓扑下研究定义在紧距离空间上具有普遍意义的最优化问题的稳定性;指出在Baire分类意义下大多数这类问题的解是通有稳定的。     

编队协同反导决策方案优选

为了充分地调度好编队全部的抗导资源,实现抗导弹群的成功概率最大,在利用随机服务系统理论对编队协同反导决策方案各指标值进行计算的基础上,将"模糊优选理论"与"协同学原理"有机地结合起来,求解了在反舰导弹到达强度与舰空导弹的单发杀伤概率在不同值的情况下,编队协同反导决策方案的隶属度,为编队指挥员进行反导决策方案的优选提供了科学依据。     

采用多种群搜索策略的微粒群算法调整消磁绕组

目前,常用的消磁系统调整方法一般都是针对舰艇磁场进行的,而较少考虑磁场梯度。为改善舰艇的磁隐身性能,应当同时考虑两者的有效衰减,由此确定的电流优化调整问题为多目标优化问题。为解决该问题,首先采用一种基于多种群搜索策略的微粒群算法分别求解两类目标函数的最优值,并将这两类最优值作为目标函数的归一化常数,然后选取一组适当的权重将多目标优化问题转化为单目标优化问题,并采用多种群微粒群算法求解该单目标优化问题。最后,采用一个典型的消磁系统优化调整问题来验证方法的有效性。实验结果表明:该方法不但能够实现舰船磁信号的削减,同时还能有效抑制舰船磁信号的波动。