并行量子遗传算法在QoS组播路由中的应用

随着网络通信技术的发展和Internet的普及,性能出色的组播路由越来越重要.著名的组播路由Steiner树问题是NP完全问题,应采用启发式方法求解.文中在常规量子遗传算法中引入并行进化模型,提出了一种解决多约束QoS组播路由优化问题的算法.在满足带宽、时延约束条件下寻找代价最小的组播树,并合理安排节点负荷,减少通信开销.仿真实验结果表明本算法搜索速度快、全局寻优能力强,性能和效率优于常规量子遗传算法.     

多元约束混合搜索算法研究

解空间搜索是约束求解的关键环节. 目前较为常用的搜索算法一般是基于二元约束或单一搜索策略设计的. 本文设计了六个基于多元约束的混合搜索算法(BM_GASBJ, BM_GBJ, BM_CBJ, FC_GASBJ, FC_GBJ, FC_CBJ), 它们分别混合同一类搜索策略中不同算法或不同类搜索策略; 分析并给出了不同混合算法的性能差异. 系统测试结果表明混合搜索算法明显提高了解搜索效率和约束求解系统的性能.     

协同设计中定量化约束求解方法

通过对约束满足与约束冲突的分析，提出了约束求解的定量化策略．基于变量不确定性，量化了约束满足程度与约束冲突程度，解决了约束求解过程中的优先权问题；给出了约束变化量及关联函数，为约束求解确立了具体的目标和实施方法，实现了约束求解过程的有序搜索．定量化约束求解策略不仅实现了对约束的有序及有效求解，而且真正地实现了在上游约束求解过程中定量地考虑下游约束求解问题．最后，利用随机仿真技术实现了基于变量不确定性的约束求解策略的验证．     

带有通配符和长度约束的模式匹配问题求解模型

讨论了带有通配符和长度约束的模式匹配(PMWL)问题,其中模式由子模式序列集组成,两个相邻子模式的间隔在一定长度范围内。针对PMWL问题,已有工作包括设计启发式求解算法和对特殊情况进行完备性分析,然而还需要构建问题的基础求解模型。借鉴约束可满足问题框架,构建了由变量、值域和约束组成的三元组求解模型,对PMWL问题的基本概念和基本性质给出了形式化描述。最后,给出了算法求解PMWL问题的特定条件下的完备解。     

求解仿真优化问题的知识型启发式搜索方法

仿真优化研究基于仿真的目标优化问题,已经成为系统仿真和运筹学等领域共同关注的热点和前沿课题.针对离散事件动态系统仿真优化中的难点问题,提出了一种全新的知识型启发式搜索方法.采用知识模型和启发式搜索模型相结合的集成建模思路,以启发式搜索模型为基础,同时突出知识模型的作用,将启发式搜索模型和知识模型进行优化组合、优势互补,以提高启发式搜索技术的效率.基于期望值模型的数值仿真,验证了方法的可行性和有效性.仿真结果表明,无论是求解质量还是求解速度,都优于其它几种现有方法.研究结果表明,将知识模型合理地嵌入到现有启发式搜索方法中,可以有效地解决复杂的仿真优化问题.     

最先失败原则的约束传播算法

约束满足问题是人工智能研究领域的重要问题.而弧相容算法是求解约束满足问题的重要工具.在弧相容算法中应用启发式规则已经证明是一种很有效的方式.本文提出一个基于最先失败原则的约束传播算法,该算法在搜索过程中更早地发现含有空域的变量并提前进行回溯,从而提高问题求解效率.同时,在"明月1.0"架构下实现了该算法,实验结果表明使用最先失败原则的弧相容算法要比原来的算法效率上提高了约40%.     

一种约束求解的两级规划方法

提出了一个基于图构造的几何约束求解方法。基于自由度分析的理论,把整个约束图分解为多个约束子图,各个约束子图之间的共享结点形成一个全局的共享结点集,当共享结点集中的结点确定下来时,相关的约束子图中的结点也相应被确定下来。通过这样的全局到局部的两级求解规划的构造,缩小了约束问题的规模,提高了求解效率。     

并行量子遗传算法在QoS组播路由中的应用

随着网络通信技术的发展和Internet的普及,性能出色的组播路由越来越重要。著名的组播路由Steiner树问题是NP完全问题,应采用启发式方法求解。文中在常规量子遗传算法中引入并行进化模型,提出了一种解决多约束QoS组播路由优化问题的算法。在满足带宽、时延约束条件下寻找代价最小的组播树,并合理安排节点负荷,减少通信开销。仿真实验结果表明本算法搜索速度快、全局寻优能力强,性能和效率优于常规量子遗传算法。     

MAS系统的问题求解能力分析

本文用状态空间搜索模型分析了多Agent系统(MAS)的问题求解能力，认为MAS系统中Agent之间知识的组合应用和对问题搜索方向的交互和决策是影响MAS系统问题求解能力的主要原因，在状态空间搜索模型下可以将Agent间知识的组合应用表达为不同Agent的搜索路径的组合，而Agent对搜索方向的判断是基于启发式信息做出的，从而为形式化分析MAS系统的性能建立了通用的模型．本文以A*算法为例探讨了可采纳算法下多Agent合作求解效果与Agent的知识和启发信息之间的关系，指出只有在一定条件下MAS系统才会获得更好的解题能力．本文还对非可采纳算法下MAS系统性能分析方法提出了初步看法．     

一种基于预排序的贝叶斯网络结构学习算法

贝叶斯网络结构学习对贝叶斯网络解决实际问题至关重要.基于评分与搜索的方法是目前比较常用的结构学习方法,但该类方法中结构搜索空间的大小随结点个数增加而指数增长,因此一般采用启发式搜索策略,有些方法还需要结点次序.在基于结点次序的最大相关-最小冗余贪婪贝叶斯网络结构学习算法中,由于是随机产生初始结点的次序,这增大了结果的不确定性.本文提出一种生成优化结点初始次序的方法,在得到基本有序的结点初始次序后,再结合近邻交换算子进行迭代搜索,能够在较短的时间内得到更加正确的贝叶斯网络结构.实验结果表明了该方法的有效性.     

Revisiting decomposition analysis of geometric constraint graphs

Geometric problems defined by constraints can be represented by geometric constraint graphs whose nodes are geometric elements and whose arcs represent geometric constraints. Reduction and decomposition are techniques commonly used to analyze geometric constraint graphs in geometric constraint solving.In this paper we first introduce the concept of deficit of a constraint graph. Then we give a new formalization of the decomposition algorithm due to Owen. This new formalization is based on preserving the deficit rather than on computing triconnected components of the graph and is simpler. Finally we apply tree decompositions to prove that the class of problems solved by the formalizations studied here and other formalizations reported in the literature is the same.     

基于商空间的产品设计问题求解模型研究

针对产品设计约束网络模型的求解难问题,提出了基于商空间的产品设计问题求解模型。将产品设计问题的约束网络转换为拓扑空间,使用等价关系对约束满足问题中的变量进行划分,建立了基于商空间的产品设计问题分层求解模型。通过商映射实现不同层次之间的映射和回溯。这种采用分层次、逐步细化的问题求解方法,降低了问题求解的计算复杂性。     

随机约束满足问题的回溯算法分析

提出一种新的随机CSP(constraint sa tisfaction problem)模型,并且通过研究搜索树的平均节点数,分析了回溯算法求解该模型 的平均复杂性.结果表明,这种模型能够生成难解的CSP实例,找到所有的解或证明无解所需的 平均节点数即随变量数的增加而指数增长.因此,该模型可以用来研究难解实例的性质和CSP 算法的性能等问题,从而有助于设计出更为高效的算法.     

A gradual neural-network approach for frequency assignment insatellite communication systems

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.     

Nogood-FC for solving partitionable constraint satisfaction problems

Many real problems can be naturally modelled as constraint satisfaction problems (CSPs). However, some of these problems are of a distributed nature, which requires problems of this kind to be modelled as distributed constraint satisfaction problems (DCSPs). In this work, we present a distributed model for solving CSPs. Our technique carries out a partition over the constraint network using a graph partitioning software; after partitioning, each sub-CSP is arranged into a DFS-tree CSP structure that is used as a hierarchy of communication by our distributed algorithm. We show that our distributed algorithm outperforms well-known centralized algorithms solving partitionable CSPs.     

CSPs with counters: a likelihood-based heuristic

Abstract. Counter constraints are a naturalrepresentation of constraints on the finite capacity of resources in resource-allocation type problems. They are a generic family of non-binary constraints that limit the number of variables that may be assigned particular values. Counter constraints can be represented by binary constraints, at a cost. We analyse the cost, show how a counter can be represented as a linear number of binary constraints, and demonstrate empirically that even with the optimal reduction,an explicit representation of counters is preferable to their representation as a set of binary constraints. For counter constraints, value ordering is essential. An heuristic for value ordering on constraint satisfaction problems (CSP), based on the estimated likelihoodof a solution, is presented. The proposed value ordering heuristic is useful for counter constraints, as well as for binary CSPs, where it can be used to approximate the number of solutions consistent with a particular value assignment to a variable. The proposed value ordering heuristic integrates counter constraints with binary constraint networks in a novel manner. Counter constraints are problematic for most heuristics, which are local in scope, yet we demonstrated empirically that the proposed value ordering heuristic is significantly superior to heuristics used in previous work.     

约束可满足性中求解RB模型实例的算法综述

约束满足问题是人工智能领域中最基本的NP完全问题之一。多年来,随着约束满足问题的深入研究,国内外学者提出多种实例模型。其中,RB模型是一种能生成具有精确相变的增长域约束满足问题实例,其求解难度极具挑战性。为了寻找其求解的新型高效算法,促进约束可满足问题的RB模型求解算法领域的研究,首先从约束满足问题的模型发展、求解技术进行分析;其次,对各类求解RB模型实例算法进行梳理,将求解的算法文献划分为回溯启发式类、信息传播类和元启发式类相关改进算法,从算法原理、改进策略、收敛性和精确度等方面进行对比综述;最后给出求解RB模型实例算法的研究趋势和发展方向。     

考虑特殊时间约束的混合流水车间调度

针对等待时间受限的准时制混合流水车间调度问题,建立其约束满足优化模型。考虑到模型具有二元变量的复杂性特点,将原问题分解为多能力流水车间调度和机器指派两个子问题。在对多能力流水车间调度问题的约束满足优化求解过程中嵌入邻域搜索,从而提高算法的收敛性。数据实验表明模型和算法是可行和有效的。     

GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method

Constraint solving has been applied to many domains of program analysis and is further used in concurrent program analysis. Concurrent programs have been widely used with the rapid development of multi-core processors. However, concurrent bugs threaten the security and reliability of concurrent programs, and thus it is of great importance to detect concurrent bugs. The explosion of thread interleaving caused by the uncertainty of the execution of concurrent program threads brings some challenges to the detection of concurrent bugs. Existing concurrent defect detection algorithms reduce the exploration cost in the state space of concurrent programs by reducing invalid thread interleaving. For example, the maximal causal model algorithm transforms the state space exploration problem of concurrent programs into a constraint solving problem. However, it will produce a large number of redundant and conflicting constraints during constraint construction, which greatly prolongs the time of constraint solving, increases the number of constraint solver calls, and reduces the exploration efficiency of concurrent program state space. Thus, this study proposes a directed graph constraint-guided maximal causality reduction method, called GC-MCR. This method aims to improve the speed of constraint solving and the efficiency of the state space exploration of concurrent programs by filtering and reducing constraints using directed graphs. The experimental results show that the GC-MCR method can effectively optimize the expression of constraints, so as to improve the solving speed of the constraint solver and reduce the number of solver calls. Compared with the existing J-MCR method, GC-MCR can significantly improve the detection efficiency of concurrent program bugs without reducing the detection ability of concurrent bugs, and the test time on 38 groups of concurrent test programs widely used by existing research methods can be reduced by 34.01% on average.     

Ants can solve constraint satisfaction problems

We describe a novel incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone information is used to guide the search, as a heuristic for choosing values to be assigned to variables. We first describe the basic ACO algorithm for solving CSPs and we show how it can be improved by combining it with local search techniques. Then, we introduce a preprocessing step, the goal of which is to favor a larger exploration of the search space at a lower cost, and we show that it allows ants to find better solutions faster. Finally, we evaluate our approach on random binary problems