校车路径问题的改进迭代局部搜索算法

针对考虑站点服务时间、学生最大乘车时间约束的校车路径问题(SBRP),提出一种改进迭代局部搜索(ILS)算法以提升求解质量。该算法使用大规模邻域搜索(LNS)算法作为扰动算子;在解的破坏过程中,设计一组解的破坏因子并赋以一定的选择概率,每隔若干次迭代后根据解的质量自适应更改破坏因子的选择概率,进而调整解的破坏程度。为提升ILS解的多样性,算法采用了基于偏差系数的邻域解接受准则。在国际基准测试案例上进行了测试,测试结果表明在ILS算法中使用自适应调整破坏程度的LNS扰动比常规扰动和其他破坏扰动的求解质量有大幅提升;与蚁群算法的比较结果进一步验证了改进算法的有效性。     

一种有效的增量顺序学习模糊神经网络

利用误差下降率定义输入数据对系统输出的敏感性,并以此作为规则产生标准,提出一种有效增量顺序学习模糊神经网络。将修剪策略引入规则产生过程,因此该算法产生的模糊神经网络不需要进行修剪。通过仿真实验,本算法在达到与其他算法相当性能的情况下,能够获得更高的准确率和更简单的结构。     

结合基因遗传和贪婪搜索的布谷鸟社区检测算法

为了提高复杂网络社区结构挖掘的精度,结合基因遗传和贪婪搜索提出一种面向模块度优化的布谷鸟社区检测算法(GGCSCA)。布谷鸟种群在有序邻居表上逐维随机游走,并采用优质基因遗传策略,使得种群高效优化,同时应用局部模块度增量最大化的贪婪偏好搜索算法快速提升种群质量,以取得好的社区划分结果。GGCSCA在基准网络和经典网络上进行了实验,并与一些典型算法进行对比,结果说明了本社区发现算法的有效性、准确性和快速收敛性,具有较强的社区识别能力,能够精细地检测出网络社区结构。     

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time.     

Morgan's problem of Boolean control networks

This paper investigates the Morgan''s problem of Boolean control networks. Based on the matrix expression of logical functions, two key steps are proposed to solve the problem. First, the Boolean control network is converted into an output-decomposed form by constructing a set of consistent output-friendly subspaces, and a necessary and sufficient condition for the existence of the consistent output-friendly subspaces is obtained. Secondly, a type of state feedback controllers are designed to solve the Morgan''s problem if it is solvable. By solving a set of matrix equations, a necessary and sufficient condition for converting an output-decomposed form to an input-output decomposed form is given, and by verifying the output controllability matrix, the solvability of Morgan''s problem is obtained.     

A fuzzy curved search algorithm for neural network learning

In this paper, we develop a curved search algorithm which uses second-order information, for the learning algorithm for a supervised neural network. With the objective of reducing the training time, we introduce a fuzzy controller for adjusting the first and second-order approximation parameters in the iterative method to further reduce the training time and to avoid the spikes in the learning curve which sometimes occurred with the fixed step length. Computational results indicate a significant reduction in training when comparing with the delta learning rule.     

Parameter estimation for Boolean models of biological networks

Boolean networks have long been used as models of molecular networks, and they play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data.     

A tabu search algorithm for the multi-period inspector scheduling problem

This paper introduces a multi-period inspector scheduling problem (MPISP), which is a new variant of the multi-trip vehicle routing problem with time windows (VRPTW). In the MPISP, each inspector is scheduled to perform a route in a given multi-period planning horizon. At the end of each period, each inspector is not required to return to the depot but has to stay at one of the vertices for recuperation. If the remaining time of the current period is insufficient for an inspector to travel from his/her current vertex A to a certain vertex B, he/she can choose either waiting at vertex A until the start of the next period or traveling to a vertex C that is closer to vertex B. Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time. We first describe an approach of computing the shortest transit time between any pair of vertices with an arbitrary departure time. To solve the MPISP, we then propose several local search operators adapted from classical operators for the VRPTW and integrate them into a tabu search framework. In addition, we present a constrained knapsack model that is able to produce an upper bound for the problem. Finally, we evaluate the effectiveness of our algorithm with extensive experiments based on a set of test instances. Our computational results indicate that our approach generates high-quality solutions.     

An efficient mechanism for processing similarity search queries in sensor networks

The similarity search problem has received considerable attention in database research community. In sensor network applications, this problem is even more important due to the imprecision of the sensor hardware, and variation of environmental parameters. Traditional similarity search mechanisms are both improper and inefficient for these highly energy-constrained sensors. A difficulty is that it is hard to predict which sensor has the most similar (or closest) data item such that many or even all sensors need to send their data to the query node for further comparison. In this paper, we propose a similarity search algorithm (SSA), which is a novel framework based on the concept of Hilbert curve over a data-centric storage structure, for efficiently processing similarity search queries in sensor networks. SSA successfully avoids the need of collecting data from all sensors in the network in searching for the most similar data item. The performance study reveals that this mechanism is highly efficient and significantly outperforms previous approaches in processing similarity search queries.     

An Efficient Optimization Procedure for Tetrahedral Meshes by Chaos Search Algorithm

A simple and efficient local optimization-based procedure for node reposition-ing/smoothing of three-dimensional tetrahedral meshes is presented.The initial tetrahedral mesh is optimized with respect to a specified element shape measure by chaos search algorithm,which is very effective for the optimization problems with only a few design variables.Examples show that the presented smoothing procedure can provide favorable conditions for local transformation approach and the quality of mesh can be significantly improved by the combination of these two procedures with respect to a specified element shape measure.Meanwhile,several commonly used shape measures for tetrahedral element,which are considered to be equivalent in some weak sense over a long period of time,are briefly re-examined in this paper.Preliminary study indicates that using different measures to evaluate the change of element shape will probably lead to inconsistent result for both well shaped and poorly shaped elements.The proposed smoothing approach can be utilized as an appropriate and effective tool for evaluating element shape measures and their influence on mesh optimization process and optimal solution.     

Simultaneous stabilization for a set of Boolean control networks

This paper investigates simultaneous stabilization of a collection of Boolean control networks (BCNs) by using the semi-tensor product method, and presents a number of new results. First, an algebraic expression of the BCNs is obtained by the semi-tensor product, based on which some necessary and sufficient conditions are presented to solve the simultaneous stabilization problem by a free control sequence, a state-feedback control, and an output-feedback control, respectively. Second, using the column stacking form of matrices, a new procedure is established to design both state-feedback and output-feedback controllers for the simultaneous stabilization problem. The study of two illustrative examples shows that the new results obtained in this paper are very effective in solving simultaneous stabilization of a collection of BCNs.     

状态翻转控制下布尔控制网络的可镇定性和Q学习算法

在给定一个子集的条件下, 本文研究了在状态翻转控制下布尔控制网络的全局镇定问题. 对于节点集的给定子 集, 状态翻转控制可以将某些节点的值从1 (或0)变成0 (或1). 将翻转控制作为控制之一, 本文研究了状态翻转控制下的 布尔控制网络. 将控制输入和状态翻转控制结合, 提出了联合控制对和状态翻转转移矩阵的概念. 接着给出了状态翻转 控制下布尔控制网络全局稳定的充要条件. 镇定核是最小基数的翻转集合, 本文提出了一种寻找镇定核的算法. 利用可 达集的概念, 给出了一种判断全局镇定和寻找联合控制对序列的方法. 此外, 如果系统是一个大型网络, 则可以利用一 种名为Q学习算法的无模型强化学习方法寻找联合控制对序列. 最后给出了一个数值例子来说明本文的理论结果.     

Feedback control design for the complete synchronisation of two coupled Boolean networks

In the literatures, to design state feedback controllers to make the response Boolean network synchronise with the drive Boolean network is rarely considered. Motivated by this, feedback control design for the complete synchronisation of two coupled Boolean networks is investigated in this paper. A necessary condition for the existence of a state feedback controller achieving the complete synchronisation is established first. Then, based on the necessary condition, the feedback control law is proposed. Finally, an example is worked out to illustrate the proposed design procedure.     

Categorical data fuzzy clustering: An analysis of local search heuristics

The fuzzy c partition of a set of qualitative data is the problem of selecting the optimal c   centroids that are the most representative of the whole population. Moreover, a set of weights _wij$w_{\mathit{ij}}$ must be determined, describing the fuzzy membership function of pattern i to the cluster represented by centroid j. Both problems are formulated by a single mathematical programming problem, that is an extension of the classic p-median models often used for clustering. The new objective function is neither concave nor convex and the application requires the clustering of many thousands of data, therefore heuristic methods are to be developed to find the best fuzzy partition.     

Efficient distributed data scheduling algorithm for data aggregation in wireless sensor networks

With the rapid development of applications for wireless sensor networks, efficient data aggregation methods are becoming increasingly emphasized. Many researchers have studied the problem of reporting data with minimum energy cost when data is allowed to be aggregated many times. However, some aggregation functions used to aggregate multiple data into one packet are unrepeatable; that is, every data is aggregated only at most once. This problem motivated us to study reporting data with minimum energy cost subject to that a fixed number of data are allowed to be aggregated into one packet and every data is aggregated at most once. In this paper, we propose novel data aggregation and routing structures for reporting generated data. With the structures, we study the problem of scheduling data to nodes in the networks for data aggregation such that the energy cost of reporting data is minimized, termed MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING. In addition, we show that MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING is NP-complete. Furthermore, a distributed data scheduling algorithm is proposed accordingly. Simulations show that the proposed algorithm provides a good solution for MINIMUM ENERGY-COST DATA-AGGREGATION SCHEDULING.     

An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems

This paper proposes an effective hybrid tabu search algorithm (HTSA) to solve the flexible job-shop scheduling problem. Three minimization objectives – the maximum completion time (makespan), the total workload of machines and the workload of the critical machine are considered simultaneously. In this study, a tabu search (TS) algorithm with an effective neighborhood structure combining two adaptive rules is developed, which constructs improved local search in the machine assignment module. Then, a well-designed left-shift decoding function is defined to transform a solution to an active schedule. In addition, a variable neighborhood search (VNS) algorithm integrating three insert and swap neighborhood structures based on public critical block theory is presented to perform local search in the operation scheduling component. The proposed HTSA is tested on sets of the well-known benchmark instances. The statistical analysis of performance comparisons shows that the proposed HTSA is superior to four existing algorithms including the AL + CGA algorithm by Kacem, Hammadi, and Borne (2002b), the PSO + SA algorithm by Xia and Wu (2005), the PSO + TS algorithm by Zhang, Shao, Li, and Gao (2009), and the Xing’s algorithm by Xing, Chen, and Yang (2009a) in terms of both solution quality and efficiency.     

An efficient algorithm for mobile localization in sensor networks

Node localization is a fundamental problem in wireless sensor network.There are many existing algorithms to estimate the locations of the nodes.However,most of the methods did not consider the presence of obstacles.In practice,obstacles will lead to blockage and reflection of communication signals between sensor nodes.Therefore,the presence of obstacles will greatly affect the localization result.In this paper,we implement an obstacle-handling algorithm based on the localization tool developed by MIT,The experimental result shows that the enhanced algorithm can reduce the average distance error by up to 46%,compared to the original algorithm.     

An improved cuckoo search algorithm for the problem of electric distribution network reconfiguration

Power loss reduction has an important role in operating electric distribution network system. There are a lot of methods for reduction power loss such as capacitor placement, distributed generation placement and electric distribution network reconfiguration (EDNR). Among these methods, the EDNR is an efficient technique to reduce power loss due to without taking any costs. However, the EDNR problem is a nonlinear, discrete problem and lots of extreme points. Therefore, it is necessary to have efficient methods for solving the EDNR problem. In this paper, an improved cuckoo search algorithm (ICSA) is proposed for solving the EDNR problem. In which, based on disadvantages of exploration and exploitation process of cuckoo search algorithm (CSA) for solving the EDNR problem, a local search mechanism is added to exploit candidate solutions existing around the current best solution. The calculated results on the simple distribution networks to complex distribution networks show that ICSA has ability for finding the global optimal solution with much smaller iterations and better quality of obtained solution compared with CSA and some other improved versions of CSA. The performance comparisons with other existing methods available in previous studies and the software of Power System Simulator/Advanced Distribution Engineering Productivity Tool (PSS/ADEPT) also lead to the better electric distribution network configuration with smaller total power losses. As a result, ICSA is a potential and reliable method for solving the EDNR problems.