协同空对地攻击中的目标分配方法

多机编队协同空对地打击是航空兵作战的一种主要样式,目标分配是制定协同空对地打击任务计划的一项重要内容,其目的是充分发挥编队内成员的优势,实现资源的优化配置,提高作战效能,减小攻击代价.分析了目标分配中的目标覆盖约束、毁伤概率阈值约束以及任务平衡度约束,建立了以实现最大攻击效费比为目标,并充分考虑目标分布状况的目标分配模型,设计了基于遗传算法的目标分配算法,以典型的空对地攻击任务为背景对方法进行了仿真,仿真结果表明,模型可以有效地解决多机协同对地攻击中的目标分配问题.     

无人机编队超视距空战决策及作战仿真

以无人机的超视距空战为研究背景,建立一种综合态势评估、目标分配和损耗裁定的协同空战仿真模型。首先,综合友机位置和敌方战机威力对我机的态势影响,提出一种基于人工势场的态势评估方法;然后,利用文化基因算法进行目标分配,分别采用离散差分进化算法和邻域搜索算法作为其全局和局部搜索策略;最后,用两步裁定法模拟空战双方的相互攻击,实现超视距空战中的损耗裁定。仿真结果验证了所提出模型的合理性和算法的有效性。     

基于灰色分析的对地多目标攻击战术规划研究

对地多目标攻击时,目标威胁度评估可以为武器资源的有效配置提供重要依据。根据威胁评估和多属性决策的特点,运用灰色关联分析理论对目标威胁度评估进行研究,建立基于灰色关联分析理论的对地多目标攻击决策模型,使对地攻击决策问题转化为对目标威胁度的求解,通过求解该模型即可获得目标威胁度的排序。然后根据协同优先权对多机对地多目标攻击进行目标分配和攻击排序。通过仿真说明该模型的合理性和有效性,从而为对地多目标攻击提供一种有效的决策方法。     

基于改进鱼群算法的多无人机任务分配研究

多无人机协同任务分配问题是多无人机协同控制的关键,为解决单目标函数构建的任务分配模型不能满足决策者对战场环境大量信息的需求,以最大航程和最长任务执行时间作为多无人机任务分配的两个目标函数,依据多目标优化理论,建立了协同任务分配多目标优化模型.并采用了一种借鉴遗传算法中的变异思想的改进鱼群算法进行求解,得到多无人机任务分配的多目标最优解集,然后根据决策者的偏好选择最佳任务分配方案.最后将上述算法应用于多无人机协同任务分配中并进行了仿真,仿真结果验证了改进鱼群算法的收敛性及有效性,为多无人机协同任务分配优化提供了参考依据.     

一种基于攻击树的VANET位置隐私安全风险评估的新方法*

为了系统分析评估车载自组织网络中位置隐私所面临的风险,建立了以位置隐私泄露为攻击目标的攻击树模型方法,在攻击树的基础上采用布尔代数法求出各种可能的攻击序列,并通过对叶子节点进行多属性赋值最后计算出了系统总的风险值和各攻击序列发生的概率,由此为决策者采取相应的位置隐私保护措施提供依据。     

协同干扰节点资源优化分配模型及算法

针对协同干扰节点资源优化分配问题,通过引入协同干扰组的概念,建立了协同干扰节点优化分组模型,将系统有效干扰时间优化简化为协同干扰节点分组数优化,并考虑了协同干扰组对目标通信压制的误码率约束,保证有效压制干扰下系统工作时间最长。结合智能优化算法,提出了基于遗传算法的干扰机节点分组优化求解方法,给出了具体的求解步骤,并与贪婪算法结果进行比较。最后通过实例仿真验证了方法的有效性和优越性。     

基于聚类的关键节点识别

网络对抗是现代战争的关键因素,对抗手段主要是攻击节点,攻击关键节点能够用最少的资源最大程度地破坏敌方通信系统.现有研究大多通过挖掘节点在网络拓扑中的地位来分析节点的重要程度.这种方法往往计算复杂,不适用于大规模网络.提出通过聚类算法进行关键节点识别,对节点进行密度峰值聚类,得到以局部密度和与更高密度点的距离为坐标的决策...     

用遗传算法实现防空导弹体系的目标分配

根据目前防空作战的特点,对自动化指挥控制系统中的目标分配问题进行了详细分析,从而建立了对单个空袭目标和多个空袭目标不同空袭敌情时的目标分配模型,构造了独特的模型参数,根据一例来袭空情,通过改进的遗传算法对模型进行求解,得到了比较好的目标分配结果,从而为防空导弹武器系统的目标分配问题提供了一个可行的参考方法。     

基于攻击场景的信息系统生存性计算

介绍一种基于攻击场景的系统生存性量化评估方法。该方法基于网络系统的生存性,不仅和本身有关,还与系统所处的环境密切相关。通过攻击图确定攻击场景,给出网络系统生存性的量化公式,帮助管理员有效确定影响系统生存性的关键节点,通过试验验证了该方法的有效性。     

多导弹时间协同分布式导引律设计

为了实现多导弹协同攻击同一目标, 提出一种时间协同分布式导引律设计方法, 该方法对于无角度约束和有角度约束的情况均适用. 首先, 以各导弹子系统作为网络同构节点建立了多导弹时间协同制导模型, 该模型以各导弹剩余时间信息附加预定偏置量为输出; 然后, 以调整子系统间输出误差为目的, 设计了系统的分布式控制方案, 并结合一般协同控制系统的收敛性条件, 分析了导弹寻的导引的特殊性, 给出了保证多导弹时间协同的通信拓扑条件; 最后, 通过仿真结果验证了所提出方法的有效性.

     

On bilevel multi-follower decision making: General framework and solutions

Within the framework of any bilevel decision problem, a leader’s decision is influenced by the reaction of his or her follower. When multiple followers who may have had a share in decision variables, objectives and constraints are involved in a bilevel decision problem, the leader’s decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. This paper firstly identifies nine different kinds of relationships (S₁ to S₉) amongst followers by establishing a general framework for bilevel multi-follower decision problems. For each of the nine a corresponding bilevel multi-follower decision model is then developed. Also, this paper particularly proposes related theories focusing on an uncooperative decision problem (i.e., S₁ model), as this model is the most basic one for bilevel multi-follower decision problems over the nine kinds of relationships. Moreover, this paper extends the Kuhn-Tucker approach for driving an optimal solution from the uncooperative decision model. Finally, a real case study of a road network problem illustrates the application of the uncooperative bilevel decision model and the proposed extended Kuhn-Tucker approach.     

Rule sets based bilevel decision model and algorithm

Bilevel decision addresses the problem in which two levels of decision makers, each tries to optimize their individual objectives under certain constraints, act and react in an uncooperative, sequential manner. As bilevel decision making often involves many uncertain factors in real world problems, it is hard to formulate the objective functions and constraints of the leader and the follower in modelling a real bilevel decision problem. This study explores a new approach that uses rule sets to formulate a bilevel decision problem. It first develops related theories to prove the feasibility to model a bilevel decision problem by rule sets. It then proposes an algorithm to describe the modelling process. A case study is discussed to illustrate the functions and effectiveness of the proposed rule sets based bilevel decision modelling algorithm.     

Constraint programming approach to a bilevel scheduling problem

Bilevel optimization problems involve two decision makers who make their choices sequentially, either one according to its own objective function. Many problems arising in economy and management science can be modeled as bilevel optimization problems. Several special cases of bilevel problem have been studied in the literature, e.g., linear bilevel problems. However, up to now, very little is known about solution techniques of discrete bilevel problems. In this paper we show that constraint programming can be used to model and solve such problems. We demonstrate our first results on a simple bilevel scheduling problem.     

AN ALGORITHM FOR SOLVING RULE SETS–BASED BILEVEL DECISION PROBLEMS

Bilevel decision addresses the problem in which two levels of decision makers each tries to optimize their individual objectives under certain constraints, and to act and react in an uncooperative and sequential manner. Given the difficulty of formulating a bilevel decision problem by mathematical functions, a rule sets–based bilevel decision (RSBLD) model was proposed. This article presents an algorithm to solve a RSBLD problem. A case‐based example is given to illustrate the functions of the proposed algorithm. Finally, a set of experiments is analyzed to further show the functions and the effectiveness of the proposed algorithm.     

An interval programming approach for the bilevel linear programming problem under fuzzy random environments

In this paper, we address a class of bilevel linear programming problems with fuzzy random variable coefficients in objective functions. To deal with such problems, we apply an interval programming approach based on the $\alpha $ -level set to construct a pair of bilevel mathematical programming models called the best and worst optimal models. Through expectation optimization model, the best and worst optimal problems are transformed into the deterministic problems. By means of the Kth best algorithm, we obtain the best and worst optimal solutions as well as the corresponding range of the objective function values. In this way, more information can be provided to the decision makers under fuzzy random circumstances. Finally, experiments on two examples are carried out, and the comparisons with two existing approaches are made. The results indicate the proposed approaches can get not only the best optimal solution (ideal solution) but also the worst optimal solution, and is more reasonable than the existing approaches which can only get a single solution (ideal solution).     

Bilevel model for production–distribution planning solved by using ant colony optimization

This paper addresses a hierarchical production–distribution planning problem. There are two different decision makers controlling the production and the distribution processes, respectively, that do not cooperate because of different optimization strategies. The distribution company, which is the leader of the hierarchical process, controls the allocation of retailers to each depot and the routes which serve them. In order to supply items to retailers, the distribution company orders from the manufacturing company the items which have to be available at the depots. The manufacturing company, which is the follower of the hierarchical process, reacts to these orders deciding which manufacturing plants will produce them. A bilevel program is proposed to model the problem and an ant colony optimization based approach is developed to solve the bilevel model. In order to construct a feasible solution, the procedure uses ants to compute the routes of a feasible solution of the associated multi-depot vehicle route problem. Then, under the given data on depot needs, the corresponding production problem of the manufacturing company is solved. Global pheromone trail updating is based on the leader objective function, which involves costs of sending items from depots to retailers and costs of acquiring items from manufacturing plants and unloading them into depots. A computational experiment is carried out to analyze the performance of the algorithm.     

决策者剔除的重复群决策方法

针对同样决策场景下对多组决策方案反复评估的决策问题, 建立对无效决策者剔除以及确定决策结果的模型. 使用优化模型确定指标权重, 提出决策者评价向量差异度的衡量方法, 由此对无效决策者甄别和剔除, 在此基础上建立基于灰靶的决策模型以确定最优方案, 并建立下次决策时的决策者权重计算方法. 模型充分利用历史信息, 对决策者进行评价约束, 可以有效提高决策质量. 最后通过实例表明了所提出算法的有效性和可行性.

     

A bilevel mixed-integer program for critical infrastructure protection planning

Vulnerability to sudden service disruptions due to deliberate sabotage and terrorist attacks is one of the major threats of today. In this paper, we present a bilevel formulation of the r-interdiction median problem with fortification (RIMF). RIMF identifies the most cost-effective way of allocating protective resources among the facilities of an existing but vulnerable system so that the impact of the most disruptive attack to r unprotected facilities is minimized. The model is based upon the classical p-median location model and assumes that the efficiency of the system is measured in terms of accessibility or service provision costs. In the bilevel formulation, the top level problem involves the decisions about which facilities to fortify in order to minimize the worst-case efficiency reduction due to the loss of unprotected facilities. Worst-case scenario losses are modeled in the lower-level interdiction problem. We solve the bilevel problem through an implicit enumeration (IE) algorithm, which relies on the efficient solution of the lower-level interdiction problem. Extensive computational results are reported, including comparisons with earlier results obtained by a single-level approach to the problem.     

上层含约束条件且具有模糊决策变量的二层多随从线性规划

提出并研究了一类上层含约束条件且具有模糊决策变量的二层多随从线性规划模型,利用结构元理论证明了该模型最优解等价于上层含约束条件的二层多随从线性规划模型最优解,利用Kuhn-Tucker方法得到了该模型最优解,并通过数值算例验证了该方法的可行性。     

Decentralized multi-objective bilevel decision making with fuzzy demands

Decisions in a decentralized organization often involve two levels. The leader at the upper level attempts to optimize his/her objective but is affected by the follower; the follower at the lower level tries to find an optimized strategy according to each of possible decisions made by the leader. When model a real-world bilevel decision problem, it also may involve fuzzy demands which appear either in the parameters of objective functions or constraints of the leader or the follower or both. Furthermore, the leader and the follower may have multiple conflict objectives that should be optimized simultaneously in achieving a solution. This study addresses both fuzzy demands and multi-objective issues and propose a fuzzy multi-objective bilevel programming model. It then develops an approximation branch-and-bound algorithm to solve multi-objective bilevel decision problems with fuzzy demands. Finally, two case-based examples further illustrate the proposed model and algorithm.