软计算方法在数据挖掘中的应用

对数据挖掘和软计算方法做了一个综合性的阐述,分析了模糊逻辑、神经网络、神经一模糊、遗传算法、粗糙集和混合方法等不同软计算方法的特点,尤其是对模糊逻辑软计算方法进行了更多的探讨,并结合软计算方法在数据挖掘中的应用现状,指出了数据挖掘面临的挑战和软计算方法的应用前景。     

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.     

Solving a bus driver scheduling problem with randomized multistart heuristics

We proposed several hybridizations of Reactive GRASP (Greedy Randomized Adaptive Search Procedure) with Path‐Relinking (PR) and Variable Neighborhood Search to determine high‐quality solutions for a Bus Driver Scheduling Problem (BDSP) under special constraints imposed by Italian transportation rules and mathematical statement originated by our collaboration with PluService Srl, leading Italian group in software for transportation companies. Experimental results are reported for all different proposed techniques that have been tested both on Italian real‐world instances and random instances described and used by Huisman et al. (2005).     

A model induced max-min ant colony optimization for asymmetric traveling salesman problem

A large number of hybrid metaheuristics for asymmetric traveling salesman problem (ATSP) have been proposed in the past decades which produced better solutions by exploiting the complementary characteristics of different optimization strategies. However, most of the hybridizations are criticized due to lacking of sufficient analytical basis. In this paper, a model induced max-min ant colony optimization (MIMM-ACO) is proposed to bridge the gap between hybridizations and theoretical analysis. The proposed method exploits analytical knowledge from both the ATSP model and the dynamics of ACO guiding the behavior of ants which forms the theoretical basis for the hybridization. The contribution of this paper mainly includes three supporting propositions that lead to two improvements in comparison with classical max-min ACO optimization (MM-ACO): (1) Adjusted transition probabilities are developed by replacing the static biased weighting factors with the dynamic ones which are determined by the partial solution that ant has constructed. As a byproduct, nonoptimal arcs will be indentified and excluded from further consideration based on the dual information derived from solving the associated assignment problem (AP). (2) A terminal condition is determined analytically based on the state of pheromone matrix structure rather than intuitively as in most traditional hybrid metaheuristics. Apart from the theoretical analysis, we experimentally show that the proposed algorithm exhibits more powerful searching ability than classical MM-ACO and outperforms state of art hybrid metaheuristics.     

A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem

This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH–EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.     

Artificial intelligence heuristics in solving vehicle routing problems with time window constraints

This paper describes the authors’ research on various heuristics in solving vehicle routing problem with time window constraints (VRPTW) to near optimal solutions. VRPTW is NP-hard problem and best solved to near optimum by heuristics. In the vehicle routing problem, a set of geographically dispersed customers with known demands and predefined time windows are to be served by a fleet of vehicles with limited capacity. The optimized routines for each vehicle are scheduled as to achieve the minimal total cost without violating the capacity and time window constraints. In this paper, we explore different hybridizations of artificial intelligence based techniques including simulated annealing, tabu search and genetic algorithm for better performance in VRPTW. All the implemented hybrid heuristics are applied to solve the Solomon's 56 VRPTW with 100-customer instances, and yield 23 solutions competitive to the best solutions published in literature according to the authors’ best knowledge.     

DNA计算机中二叉树的链式存储结构*

利用DNA分子和连接酶的生物特性,提出DNA计算机中二叉树的链式存储结构的设计方法,并给出二叉树链式存储结构的形式描述。在连接酶的作用下,各节点之间产生杂交和连接反应形成DNA双链,其中用到的生物技术在实验室中都能实现。为了验证方法的可行性,给出一棵二叉树的链式存储结构实例,实例表明该设计方法构造的DNA双链对应于二叉树的中序遍历序列。     

一种带有摄动的导向性蚁群算法

针对加入导向性局部搜索(Guided Local Search,GLS)的蚁群算法(Ant Colony Optimization,ACO)容易过早收敛的问题,提出一种带有摄动的导向性蚁群算法(Perturbation Guided Ant Colony Optimization,PGACO),该算法在当前解表现出过早收敛的趋势时,采用摄动(Perturbation)方式干扰解构建过程,使当前解移动到其邻域空间,从而产生一个新的可行解来避免算法过早收敛,提高算法求解的精度。实验结果表明,PGACO能有效地改善过早收敛问题,获得更优的可行解和执行速度,同时具有更强的全局搜索能力,能进一步提高算法的性能。     

Genetic algorithms for flowshop scheduling problems

In this paper, we apply a genetic algorithm to flowshop scheduling problems and examine two hybridizations of the genetic algorithm with other search algorithms. First we examine various genetic operators to design a genetic algorithm for the flowshop scheduling problem with an objective of minimizing the makespan. By computer simulations, we show that the two-point crossover and the shift change mutation are effective for this problem. Next we compare the genetic algorithm with other search algorithms such as local search, taboo search and simulated annealing. Computer simulations show that the genetic algorithm is a bit inferior to the others. In order to improve the performance of the genetic algorithm, we examine the hybridization of the genetic algorithms. We show two hybrid genetic algorithms: genetic local search and genetic simulated annealing. Their high performance is demonstrated by computer simulations.     

Evolvable computing by means of evolvable components

Encoding of information in DNA-, RNA- and other biomolecules is animportant area of research in fields such as DNA computing,bioinformatics, and, conceivably, microbiology and genetics. This surveyfocuses on two fundamental problems, the codeword design problemand the representation problem of abiotic information, formassively parallel processing with DNA molecules. The first problemrequires libraries of DNA sequences to be designed so that specificduplexes are formed during annealing while simultaneously preventingother undesirable hybridizations from occurring in the course of acomputation in the tube. The second involves a search for efficient andcost-effective methods of representing non-biological information in DNAsequences for storage and retrieval of large amouns of data (tera- andpeta-byte scales). Two approaches are treated, namely thermodynamic andcombinatoric-computational. Both experimental and theoretical resultsare described. A reference list of major works in the area is given.Finally, some open problems deemed important for their possible impacton encoding of abiotic information representation and processing arediscussed.     

Two effective hybrid metaheuristic algorithms for minimization of multimodal functions

Global optimization problems naturally arise from many applications. We propose two hybrid metaheuristic algorithms for finding a global optimum of a continuous function. Our proposed algorithms are hybridizations of genetic algorithm (GA) and variable neighbourhood search (VNS). To increase the efficiency of our algorithms, for smooth functions we present an effective locally improving line search procedure, and for non-smooth functions, we use the simplex method proposed by Nelder and Mead. By use of the recently adopted non-parametric statistical tests of Kruskal–Wallis and Mann–Whitney for analysing the behaviour of evolutionary algorithms, we compare both the efficiency and the effectiveness of our proposed algorithms with efficiently representative metaheuristic algorithms such as the multiagent GA proposed by Liang et al., the ant colony algorithm proposed by Toksari, and the VNS of Toksari and Güner on a variety of standard test functions. Computational experiments demonstrate that our proposed algorithms are efficiently effective.     

A graph coloring constructive hyper-heuristic for examination timetabling problems

In this work we investigate a new graph coloring constructive hyper-heuristic for solving examination timetabling problems. We utilize the hierarchical hybridizations of four low level graph coloring heuristics, these being largest degree, saturation degree, largest colored degree and largest enrollment. These are hybridized to produce four ordered lists. For each list, the difficulty index of scheduling the first exam is calculated by considering its order in all lists to obtain a combined evaluation of its difficulty. The most difficult exam to be scheduled is scheduled first (i.e. the one with the minimum difficulty index). To improve the effectiveness of timeslot selection, a?roulette wheel selection mechanism is included in the algorithm to probabilistically select an appropriate timeslot for the chosen exam. We test our proposed approach on the most widely used un-capacitated Carter benchmarks and also on the recently introduced examination timetable dataset from the 2007 International Timetabling Competition. Compared against other methodologies, our results demonstrate that the graph coloring constructive hyper-heuristic produces good results and outperforms other approaches on some of the benchmark instances.     

Hybrid meta-heuristic optimization algorithms for time-domain-constrained data clustering

This paper addresses the question of time-domain-constrained data clustering, a problem which deals with data labelled with the time they are obtained and imposing the condition that clusters need to be contiguous in time (the time-domain constraint). The objective is to obtain a partitioning of a multivariate time series into internally homogeneous segments with respect to a statistical model given in each cluster.In this paper, time-domain-constrained data clustering is formulated as an unrestricted bi-level optimization problem. The clustering problem is stated at the upper level model and at the lower level the statistical models are adjusted to the set of clusters determined in the upper level. This formulation is sufficiently general to allow these statistical models to be used as black boxes. A hybrid technique based on combining a generic population-based optimization algorithm and Nelder–Mead simplex search is used to solve the bi-level model.The capability of the proposed approach is illustrated using simulations of synthetic signals and a novel application for survival analysis. This application shows that the proposed methodology is a useful tool to detect changes in the hidden structure of historical data.Finally, the performance of the hybridizations of particle swarm optimization, genetic algorithms and simulated annealing with Nelder–Mead simplex search are tested on a pattern recognition problem of text identification.     

Path lengths in tree-child time consistent hybridization networks

Hybridization networks are representations of evolutionary histories that allow for the inclusion of reticulate events like recombinations, hybridizations, or lateral gene transfers. The recent growth in the number of hybridization network reconstruction algorithms has led to an increasing interest in the definition of metrics for their comparison that can be used to assess the accuracy or robustness of these methods. In this paper we establish some basic results that make it possible the generalization to tree-child time consistent (TCTC) hybridization networks of some of the oldest known metrics for phylogenetic trees: those based on the comparison of the vectors of path lengths between leaves. More specifically, we associate to each hybridization network a suitably defined vector of ‘splitted’ path lengths between its leaves, and we prove that if two TCTC hybridization networks have the same such vectors, then they must be isomorphic. Thus, comparing these vectors by means of a metric for real-valued vectors defines a metric for TCTC hybridization networks. We also consider the case of fully resolved hybridization networks, where we prove that simpler, ‘non-splitted’ vectors can be used.     

A molecular dynamics and knowledge-based computational strategy to predict native-like structures of polypeptides

One of the main research problems in structural bioinformatics is the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The current rate at which amino acid sequences are identified increases much faster than the 3-D protein structure determination by experimental methods, such as X-ray diffraction and NMR techniques. The determination of protein structures is both experimentally expensive and time consuming. Predicting the correct 3-D structure of a protein molecule is an intricate and arduous task. The protein structure prediction (PSP) problem is, in computational complexity theory, an NP-complete problem. In order to reduce computing time, current efforts have targeted hybridizations between ab initio and knowledge-based methods aiming at efficient prediction of the correct structure of polypeptides. In this article we present a hybrid method for the 3-D protein structure prediction problem. An artificial neural network knowledge-based method that predicts approximated 3-D protein structures is combined with an ab initio strategy. Molecular dynamics (MD) simulation is used to the refinement of the approximated 3-D protein structures. In the refinement step, global interactions between each pair of atoms in the molecule (including non-bond interactions) are evaluated. The developed MD protocol enables us to correct polypeptide torsion angles deviation from the predicted structures and improve their stereo-chemical quality. The obtained results shows that the time to predict native-like 3-D structures is considerably reduced. We test our computational strategy with four mini proteins whose sizes vary from 19 to 34 amino acid residues. The structures obtained at the end of 32.0 nanoseconds (ns) of MD simulation were comparable topologically to their correspondent experimental structures.     

Comprehensive first-principles study of transition-metal substitution in the γ phase of nickel-based superalloys

In order to elucidate the role of various M-alloying additions in the fcc-type γ phase of nickel-based superalloys, we have performed a comprehensive and systematical computation on all 3d-, 4d- and 5d-M alloying additions substituting in the Ni lattice using the same standard of the first-principles calculations within the framework of the density functional theory. The results show that the substitution enthalpies of Sc, Ti, V, Mn, Fe, Co, Zn, Zr, Nb, Hf, Ta, and Pt are negative, while the others elements have positive substitution enthalpies. To further explore the contributions of various factors to the substitution enthalpy, we have attempted to divide the substitution enthalpy into two parts. The first one is the mechanical deformation energy caused by the atomic volume change because of the M-alloying additions substituting in fcc Ni and the other one is the chemical and magnetic energy through electronic hybridizations and local magnetic interactions. It is found that the substitution enthalpy is a consequence of the balancing of these two contributions. Furthermore, we have attempted to correlate their mechanical deformation energies with metallic atomic radii of substitutional M-alloying addition with respect to Ni, and the chemical and magnetic energies with transferred charges between M-alloying addition and Ni, which are indeed associated with the so-called electronegativity difference.     

Constructing the Simplest Possible Phylogenetic Network from Triplets

A phylogenetic network is a directed acyclic graph that visualizes an evolutionary history containing so-called reticulations such as recombinations, hybridizations or lateral gene transfers. Here we consider the construction of a simplest possible phylogenetic network consistent with an input set T, where T contains at least one phylogenetic tree on three leaves (a triplet) for each combination of three taxa. To quantify the complexity of a network we consider both the total number of reticulations and the number of reticulations per biconnected component, called the level of the network. We give polynomial-time algorithms for constructing a level-1 respectively a level-2 network that contains a minimum number of reticulations and is consistent with T (if such a network exists). In addition, we show that if T is precisely equal to the set of triplets consistent with some network, then we can construct such a network with smallest possible level in time O(|T| ^k+1), if k is a fixed upper bound on the level of the network.     

Cubature 卡尔曼滤波与Unscented 卡尔曼滤波估计精度比较

对于不同维数下非线性系统的估计问题,为从常用的Unscented卡尔曼滤波(UKF)和Cubature卡尔曼滤波(CKF)中选取合适的滤波方法,从函数泰勒展开式和数值稳定性上对其进行了分析和比较.由于不同维数下它们捕获函数泰勒展开式高阶项的程度和数值稳定性不同,两者滤波精度出现差异,从而得到了不同维数下滤波方法的选择途径.仿真结果验证了理论分析的正确性.     

基于C／S和B／S交叉并用模式在粮食储备库安全管理中的应用

该文对C/S和B/S交叉并用模式进行了系统的研究,论述了C/S和B/S交叉并用模式的关键枝术,研究了两种模式存取同一个数据库,合并生成一套MIS系统,而不同的应用程序均可通过ODBC实现对不同的数据库的操作.这种交叉并用的模式成功地实现了从粮食进入储备库的分类、计划、仓储、调度信息发布的业务集成.