面向战争设计工程的研讨信息组织模型*

提出一种面向战争设计工程的研讨信息组织模型。该模型根据IBIS模型和Toulmin逻辑对专家发言进行结构化处理。根据研讨信息表达的意义,把研讨信息转换为问题节点、方案节点、意见节点和约束条件节点,并根据节点之间的逻辑关系,建立研讨图（DG）。其中,问题节点表示研讨问题和专家对意见和约束条件的质疑;约束条件节点反映专家对意见的判断。该模型是对研讨信息和它们之间逻辑关系的详细描述,最后阐述了该模型在战争设计工程专家群体研讨中的应用。     

带约束条件的聚类算法研究

该文描述了带约束条件的聚类和约束条件的分类。在介绍CLIQUE算法的基础上,通过对CLIQUE算法的改进,提出了一种能够在高维空间中处理实例对约束条件的聚类算法CON-CLIQUE。通过实验验证了该算法的正确性和效率。     

遗传模拟退火算法在弹药装载中的应用研究

多约束条件下的弹药装载问题是一个复杂的组合优化问题,属于NP-完全问题,其求解是很困难的。本文在考虑弹药装载中各类约束条件的情况下,将模拟退火算法作了若干改进后,融入遗传算法,提出了一种遗传模拟退火算法来求解弹药装载问题。本文对该算法的遗传算子和冷却进度表进行了详细的阐述,给出了使用该算法求解弹药装载问题的具体实现方法。     

军用物资器材集装优化算法研究

作为一类组合优化问题,多约束条件下的三维装载问题属于NP-Hard问题,其求解也是相当困难的。该文中从实际需要和具体应用出发,考虑了方向、重量、配置位置、装载优先顺序等约束条件,分析设计了物资填充策略和剩余宅间处理策略,进而提出了一种多约束条件下的三维装载启发式优化算法,用于军用物资器材的存储和集装运输管理,从而对军用物资器材的合理存储和有效运输进行优化,以提高军用物资器材的存储空间利用率和运输的经济效益。     

用遗传算法解带时延及时延抖动约束的组播路由优化问题

带约束条件的组播路由是网络应用的发展所提出的新的问题,根据不同的约束条件有不同的变种,该文讨论了带时延及时延抖动约束的组播路由优化问题,给出了该问题的数学模型,提出了求解该问题的一种基于候选路由库的遗传算法,并对该算法的仿真结果与前人的结果进行了比较。结果证明,用遗传算法解决这类问题是有效的。     

考虑任务可达约束的突击作战任务分配方法

在实际突击作战中,航空作战平台进行任务分配时存在任务可达约束的特点。为此,建立任务可达约束条件下突击作战任务分配问题的数学模型。采用遗传算法对该问题模型进行求解,并设计与之相适应的染色体编码方法、随机双点定位的交叉算子和一致性单点调整的变异算子。案例仿真结果表明,该方法能够有效解决任务可达约束条件下的任务分配问题,具有较好的稳定性和时效性。     

求解运输问题的GAPSO算法

运输问题是一个应用非常广泛的问题,传统方法对于大规模的运输问题求解比较复杂,而一些基于随机搜索算法的方法对于其约束条件的处理又比较困难.基于运输问题约束条件的特殊性,设计了一种产生可行解的方法,将对约束条件的处理转化到了算法设计之中.在此基础上,又设计了基于遗传算法和粒子群优化算法的求解运输问题的GAPSO算法,为避开对非可行解的处理,该算法对迭代过程也进行了特殊设计,从而简化了运用随机搜索算法解决运输问题的过程.最后给出了三个实例验证,通过对验证结果分析和比较,说明该算法在时间复杂度和收敛性方面都具有其优良性,是行之有效的.     

基于混合粒子群算法的烧结配料优化

在引入惩罚函数和对目标函数进行适当修改的前提下,充分利用粒子群优化算法的全局搜索能力和约束条件下共轭梯度法的局部搜索能力,设计了烧结配料优化算法．利用惩罚函数方法将约束条件优化问题转化为无约束条件优化问题,然后利用粒子群优化算法进行寻优．当群体最优信息陷入停滞时将目标函数进行适当变化,继续利用共轭梯度法进行寻优．计算结果表明,采用该方法能够在提高混合料中的有用成分、降低有害成分的前提下,更多地降低生产成本．     

实视图选择研究

定义了数据仓库领域的视图选择问题,并讨论了与该问题相关的代价模型、收益函数、代价计算、约束条件和视图索引等内容;介绍了3大类视图选择方法,即静态方法、动态方法和混合方法,以及各类方法的代表性研究成果;最后展望未来的研究方向.     

遗传算法的工程应用

分析了排课问题的各种约束条件,并研究了用遗传算法解决排课问题,给出了一个基于该算法的排课模型,并对涉及的各种问题进行探讨。     

在逐点状态约束下一个四阶线性系统的时间最优控制

在逐点状态约束下,最优控制问题的求解是很困难的,已有的最大值原理和形态规划理论很难用来求解在逐点状态约束下最优控制问题.本文讨论逐点状态约束下一个四阶线性系统的时间最优控制问题.我们采用转换的方法给出了最优时间与最优控制的具体表达式.     

Weighted Constraint Aggregation in Fuzzy Optimization

Many practical optimization problems are characterized by some flexibility in the problem constraints, where this flexibility can be exploited for additional trade-off between improving the objective function and satisfying the constraints. Fuzzy sets have proven to be a suitable representation for modeling this type of soft constraints. Conventionally, the fuzzy optimization problem in such a setting is defined as the simultaneous satisfaction of the constraints and the goals. No additional distinction is assumed to exist amongst the constraints and the goals. This paper proposes an extension of this model for satisfying the problem constraints and the goals, where preference for different constraints and goals can be specified by the decision-maker. The difference in the preference for the constraints is represented by a set of associated weight factors, which influence the nature of trade-off between improving the optimization objectives and satisfying various constraints. Simultaneous weighted satisfaction of various criteria is modeled by using the recently proposed weighted extensions of (Archimedean) fuzzy t-norms. The weighted satisfaction of the problem constraints and goals are demonstrated by using a simple fuzzy linear programming problem. The framework, however, is more general, and it can also be applied to fuzzy mathematical programming problems and multi-objective fuzzy optimization.     

圆形区域极点及方差约束下线性离散系统的控制设计

本文考虑线性离散随机系统在圆形区域极点及方差约束睛的控制器设计问题，即设计状态反馈控制器，使闭环系统同时满足预先给定的圆形区域极点约束以及预先给定的各状态分量的方差约束。文中利用代数黎卡提方程方法解决了上述问题。     

Implication and referential constraints: a new formal reasoning

The authors address the issue of reasoning with two classes of commonly used semantic integrity constraints in database and knowledge-base systems: implication constraints and referential constraints. They first consider a central problem in this respect, the IRC-refuting problem, which is to decide whether a conjunctive query always produces an empty relation on (finite) database instances satisfying a given set of implication and referential constraints. Since the general problem is undecidable, they only consider acyclic referential constraints. Under this assumption, they prove that the IRC-refuting problem is decidable, and give a novel necessary and sufficient condition for it. Under the same assumption, they also study several other problems encountered in semantic query optimization, such as the semantics-based query containment problem, redundant join problem, and redundant selection-condition problem, and show that they are polynomially equivalent or reducible to the IRC-refuting problem. Moreover, they give results on reducing the complexity for some special cases of the IRC-refuting problem     

Heuristics for the multi-period orienteering problem with multiple time windows

We present the multi-period orienteering problem with multiple time windows (MuPOPTW), a new routing problem combining objective and constraints of the orienteering problem (OP) and team orienteering problem (TOP), constraints from standard vehicle routing problems, and original constraints from a real-world application. The problem itself comes from a real industrial case. Specific route duration constraints result in a route feasibility subproblem. We propose an exact algorithm for this subproblem, and we embed it in a variable neighborhood search method to solve the whole routing problem. We then provide experimental results for this method. We compare them to a commercial solver. We also adapt our method to standard benchmark OP and TOP instances, and provide comparative tables with state-of-the-art algorithms.     

A unified aggregation and relaxation approach for stress-constrained topology optimization

In this paper, we propose a unified aggregation and relaxation approach for topology optimization with stress constraints. Following this approach, we first reformulate the original optimization problem with a design-dependent set of constraints into an equivalent optimization problem with a fixed design-independent set of constraints. The next step is to perform constraint aggregation over the reformulated local constraints using a lower bound aggregation function. We demonstrate that this approach concurrently aggregates the constraints and relaxes the feasible domain, thereby making singular optima accessible. The main advantage is that no separate constraint relaxation techniques are necessary, which reduces the parameter dependence of the problem. Furthermore, there is a clear relationship between the original feasible domain and the perturbed feasible domain via this aggregation parameter.     

自适应惩罚策略及其在交通信号优化中的应用

针对约束优化问题的求解,设计了一种处理约束条件的自适应惩罚策略,用于将具有不等式约束和等式约束的优化问题转变为仅包含决策变量上、下限约束的优化问题。该策略通过引入约束可行测度、可行度的概念来描述决策变量服从于不等式约束和等式约束的程度,并以此构造处理约束条件的自适应惩罚函数,惩罚值随着约束可行度的变化而动态自适应地改变。为了检验该惩罚策略的有效性,针对单路口交通信号优化问题进行了应用研究,并用三种不同算法进行了大量的仿真计算,结果表明所设计的自适应策略在具有高度约束条件的城市交通信号优化问题中具有良好的效果。     

Evolutionary approach on connectivity-based sensor network localization

The sensor network localization based on connectivity can be modeled as a non-convex optimization problem. It can be argued that the actual problem should be represented as an optimization problem with both convex and non-convex constraints. A two-objective evolutionary algorithm is proposed which utilizes the result of all convex constraints to provide a starting point on the location of the unknown nodes and then searches for a solution to satisfy all the convex and non-convex constraints of the problem. The final solution can reach the most suitable configuration of the unknown nodes because all the information on the constraints (convex and non-convex) related to connectivity have been used. Compared with current models that only consider the nodes that have connections, this method considers not only the connection constraints, but also the disconnection constraints. As a MOEA (Multi-Objective Evolution Algorithm), PAES (Pareto Archived Evolution Strategy) is used to solve the problem. Simulation results have shown that better solution can be obtained through the use of this method when compared with those produced by other methods.     

Neural network approach for allocation with capacity

In this paper we discuss neural network approach for allocation with capacity constraints problem. This problem can be formulated as zero-one integer programming problem. We transform this zero-one integer programming problem into an equivalent nonlinear programming problem by replacing zero-one constraints with quadratic concave equality constraints. We propose two kinds of neural network structures based on penalty function method and augmented Lagrangian multiplier method, and compare them by theoretical analysis and numerical simulation. We show that penalty function based neural network approach is not good to combinatorial optimization problem because it falls in the dilemma whether terminating at an infeasible solution or sticking at any feasible solution, and augmented Lagrangian multiplier method based neural network can alleviate this suffering in some degree.     

Circuit retiming applied to decomposed software pipelining

This paper elaborates on a new view on software pipelining, called decomposed software pipelining. The approach is to decouple the problem into resource constraints and dependence constraints. Resource constraints management amounts to scheduling an acyclic graph subject to resource constraints for which an efficiency bound is known, resulting in a bound for loop scheduling. The acyclic graph is obtained by cutting some particular edges of the (cyclic) dependence graph. In this paper, we cut edges in a different way, using circuit retiming algorithms, so as to minimize both the longest dependence path in the acyclic graph, and the number of edges in the acyclic graph. With this technique, we improve the efficiency bound given for Gasperoni and Schwlegelshohn algorithm, and we reduce the constraints that remain for the acyclic problem. We believe this framework to be of interest because it brings a new insight into the software problem by establishing its deep link with the circuit retiming problem