Practical and flexible pattern matching over Ziv–Lempel compressed text

We address the problem of string matching on Ziv–Lempel compressed text. The goal is to search for a pattern in a text without uncompressing it. This is a highly relevant issue to keep compressed text databases where efficient searching is still possible. We develop a general technique for string matching when the text comes as a sequence of blocks. This abstracts the essential features of Ziv–Lempel compression. We then apply the scheme to each particular type of compression. We present an algorithm to find all the matches of a pattern in a text compressed using LZ77. When we apply our scheme to LZ78, we obtain a much more efficient search algorithm, which is faster than uncompressing the text and then searching it. Finally, we propose a new hybrid compression scheme which is between LZ77 and LZ78, being in practice as good to compress as LZ77 and as fast to search as LZ78. We show also how to search for some extended patterns on Ziv–Lempel compressed text, such as classes of characters and approximate string matching.     

Tree template matching in unranked ordered trees

We consider the problem of tree template matching, a type of tree pattern matching, where the tree templates have some of their leaves denoted as “donʼt care”, and propose a solution based on the bottom-up technique. Specifically, we transform the tree pattern matching problem for unranked ordered trees to a string matching problem, by transforming the tree template and the subject tree to strings representing their postfix bar notation, and then propose a table-driven algorithm to solve it. The proposed algorithm is divided into two phases: the preprocessing and the searching phase. The tree template is preprocessed once, and the searching phase can be applied to many subject trees, without the need of preprocessing the tree template again. Although we prove that the space required for preprocessing is exponential in the size of the tree template in the worst case, we show that for a specific class of tree templates, the space required is linear in the size of the tree template. The time for the searching phase is linear in the size of the subject tree in the worst case. Thus, the algorithm is asymptotically optimal when one needs to search for a given tree template, of constant to logarithmic size, in many subject trees.     

多点收缩混沌优化方法及全局收敛性证明

针对目前混沌优化算法在选取局部搜索空间时的盲目性,提出一种具有自适应调节局部搜索空间能力的多点收缩混沌优化方法.该方法在当前搜索空间搜索时保留多个较好搜索点,之后利用这些点来确定之后的局部搜索空间,以达到对不同的函数和当前搜索空间内已进行搜索次数的自适应效果.给出了该算法以概率1收敛的证明.仿真结果表明该算法有效的提高了混沌优化算法的性能,改善了混沌算法的实用性.     

The suffix binary search tree and suffix AVL tree

Suffix trees and suffix arrays are classical data structures that are used to represent the set of suffixes of a given string, and thereby facilitate the efficient solution of various string processing problems—in particular on-line string searching. Here we investigate the potential of suitably adapted binary search trees as competitors in this context. The suffix binary search tree (SBST) and its balanced counterpart, the suffix AVL-tree, are conceptually simple, relatively easy to implement, and offer time and space efficiency to rival suffix trees and suffix arrays, with distinct advantages in some circumstances—for instance in cases where only a subset of the suffixes need be represented.

Construction of a suffix BST for an n-long string can be achieved in O(nh) time, where h is the height of the tree. In the case of a suffix AVL-tree this will be O(nlogn) in the worst case. Searching for an m-long substring requires O(m+l) time, where l is the length of the search path. In the suffix AVL-tree this is O(m+logn) in the worst case. The space requirements are linear in n, generally intermediate between those for a suffix tree and a suffix array.

Empirical evidence, illustrating the competitiveness of suffix BSTs, is presented.     

Some string matching problems from Bioinformatics which still need better solutions

Bioinformatics, the discipline which studies the computational problems arising from molecular biology, poses many interesting problems to the string searching community. We will describe two problems arising from Bioinformatics, their preliminary solutions, and the more general problem that they pose. The first problem is searching for α-helices in protein sequences. This particular instance of the search is based on matching of hydrophobicity/hydrophilicity. We find an algorithm which is linear in the sequence length for fixed helix length and is O(nlogn) for any helix length. The second problem is on matching probabilistic sequences against sequences or against other probabilistic sequences. In both cases we derive efficient formulas to compute scores according to a Markovian model of evolution.     

Short permutation strings

A permutation string is a string of symbols over the numerals 1, 2, …, n such that all permutations of the string 1 2 … n are subsequences. The search for short permutation strings arose out of studies into the complexity of shortest path algorithms by Karp and others. The results in the sequel are presented independent of such studies because they are felt to be of intrinsic combinatorial interest [1]. An algorithm for constructing permutation strings of length n²−2n+4 is given.     

Efficient Comparison Based String Matching

We study the exact number of symbol comparisons that are required to solve the string matching problem and present a family of efficient algorithms. Unlike previous string matching algorithms, the algorithms in this family do not "forget" results of comparisons, what makes their analysis much simpler. In particular, we give a linear-time algorithm that finds all occurrences of a pattern of length m in a text of length n in [formula] comparisons. The pattern preprocessing takes linear time and makes at most 2m comparisons. This algorithm establishes that, in general, searching for a long pattern is easier than searching for a short one. We also show that any algorithm in the family of the algorithms presented must make at least [formula] symbol comparisons, for m = 2^k − 1 and any integer k ≥ 1.     

光线寻优算法的寻优机理分析

Based on Fermat’s principle and the automatic optimization mechanism in the propagation process of light,an optimal searching algorithm named light ray optimization is presented,where the laws of refraction and reflection of light rays are integrated into searching process of optimization.In this algorithm,coordinate space is assumed to be the space that is full of media with different refractivities,then the space is divided by grids,and finally the searching path is assumed to be the propagation path of light rays.With the law of refraction,the search direction is deflected to the direction that makes the value of objective function decrease.With the law of reflection,the search direction is changed,which makes the search continue when it cannot keep going with refraction.Only the function values of objective problems are used and there is no artificial rule in light ray optimization,so it is simple and easy to realize.Theoretical analysis and the results of numerical experiments show that the algorithm is feasible and effective.     

Multiple UAVs path planning algorithms: a comparative study

Unmanned aerial vehicles (UAVs) are used in team for detecting targets and keeping them in its sensor range. There are various algorithms available for searching and monitoring targets. The complexity of the search algorithm increases if the number of nodes is increased. This paper focuses on multi UAVs path planning and Path Finding algorithms. Number of Path Finding and Search algorithms was applied to various environments, and their performance compared. The number of searches and also the computation time increases as the number of nodes increases. The various algorithms studied are Dijkstra’s algorithm, Bellman Ford’s algorithm, Floyd-Warshall’s algorithm and the AStar algorithm. These search algorithms were compared. The results show that the AStar algorithm performed better than the other search algorithms. These path finding algorithms were compared so that a path for communication can be established and monitored.     

Analysis of recursive batched interpolation search

For an ordered file of records with uniformly distributed key values, we examine an existing batched searching algorithm based on recursive use of interpolation searches. The algorithm, called Recursive Batched Interpolation Search (RBIS) in this paper, uses a divide-and-conquer technique for batched searching. The expected-case complexity of the algorithm is shown to beO(m loglog (2n/m) +m), wheren is the size of the file andm is the size of the query batch. Simulations are performed to demonstrate the savings of batched searching using RBIS. Also, simulations are performed to compare alternative batched searching algorithms which are based on either interpolation search or binary search. When the file's key values are uniformly distributed, the simulation results confirm that interpolation-search based algorithms are superior to binary-search based algorithms. However, when the file's key values are not uniformly distributed, a straight-forward batched interpolation search deteriorates quickly as the batch size increases, but algorithm RBIS still outperforms binary-search based algorithms when the batch size passes a threshold value.     

Generating optimal cutting patterns for rectangular blanks of a single size

This paper deals with the problem of minimizing trim loss in cutting rectangular blanks of a single size from a rectangular sheet using orthogonal guillotine cuts. First we prove that we can obtain the unconstrained optimal layout by searching among normal multi-section layouts. Next we present an unconstrained algorithm to search for it. The unconstrained algorithm uses a branch-and-bound method with a tight upper bound. Later we discuss the algorithm for the constrained problem where the blank demand must be met exactly. Finally, the unconstrained algorithm is extended to cope with the blade length constraint. Experimental computations show that the algorithms are extremely efficient.     

A tabu search algorithm for solving economic lot scheduling problem

The economic lot scheduling problem has driven considerable amount of research. The problem is NP-hard and recent research is focused on finding heuristic solutions rather than searching for optimal solutions. This paper introduces a heuristic method using a tabu search algorithm to solve the economic lot scheduling problem. Diversification and intensification schemes are employed to improve the efficiency of the proposed Tabu search algorithm. Experimental design is conducted to determine the best operating parameters for the Tabu search. Results show that the tabu search algorithm proposed in this paper outperforms two well known benchmark algorithms.     

Weighing Matrices and String Sorting

In this paper we establish a fundamental link between the search for weighing matrices constructed from two circulants and the operation of sorting strings, an operation that has been studied extensively in computer science. In particular, we demonstrate that the search for weighing matrices constructed from two circulants using the power spectral density criterion and exploiting structural patterns for the locations of the zeros in candidate solutions, can be viewed as a string sorting problem together with a linear time algorithm to locate common strings in two sorted arrays. This allows us to bring into bear efficient algorithms from the string sorting literature. We also state and prove some new enhancements to the power spectral density criterion, that allow us to treat successfully the rounding error effect and speed up the algorithm. Finally, we use these ideas to find new weighing matrices of order 2n and weights 2n – 13, 2n – 17 constructed from two circulants.     

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

Traditionally, the permutation flowshop scheduling problem (PFSP) was with the criterion of minimizing makespan. The permutation flowshop scheduling problem to minimize the total flowtime has attracted more attention from researchers in recent years. In this paper, a hybrid genetic local search algorithm is proposed to solve this problem with each of both criteria. The proposed algorithm hybridizes the genetic algorithm and a novel local search scheme that combines two local search methods: the Insertion Search (IS) and the Insertion Search with Cut-and-Repair (ISCR). It employs the genetic algorithm to do the global search and two local search methods to do the local search. Two local search methods play different roles in the search process. The Insertion Search is responsible for searching a small neighborhood while the Insertion Search with Cut-and-Repair is responsible for searching a large neighborhood. Furthermore, the orthogonal-array-based crossover operator is designed to enhance the GA’s capability of intensification. The experimental results show the advantage of combining the two local search methods. The performance of the proposed hybrid genetic algorithm is very competitive. For the PFSP with the total flowtime criterion, it improved 66 out of the 90 current best solutions reported in the literature in short-term search and it also improved all the 20 current best solutions reported in the literature in long-term search. For the PFSP with the makespan criterion, the proposed algorithm also outperforms the other three methods recently reported in the literature.     

A multicast routing algorithm based on searching in directed graph

Aiming at constructing a delay and delay variation bounded Steiner tree in the real-time streaming media communication, in this paper, we discuss a multicast routing algorithm based on searching a directed graph (MRASDH). During the process of the construction of the multicast tree, some nodes and links in the network topology do not affect the outcome of the constructed tree. Therefore, based on the thought of shrinking the search space through deleting these non-relative nodes and edges to the utmost, the ant algorithm is utilized to generate a directed sub-graph of the network topology for each destination node, in which each node owns a bounded out-degree. And all these sub-graphs can be merged into a new directed graph that serves as the new search space. In the new space, the simulated annealing algorithm is applied to obtain a multicast tree that satisfies the condition for the optimization. The performance analysis and simulation results demonstrate that this algorithm can effectively construct a delay and delay variation bounded multicast tree. They also show that the algorithm have lower time complexity than the current ones, which means a much better result would be achieved when the system scale rises greatly.     

A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenol oxidation in supercritical water

Based on the mechanism of biological DNA genetic information and evolution, a modified DNA genetic algorithm (MDNA-GA) is proposed to estimate the kinetic parameters of the 2-Chlorophenol oxidation in supercritical water. In this approach, DNA encoding method, choose crossover operator and frame-shift mutation operator inspired by the biological DNA are developed for improving the global searching ability. Besides, an adaptive mutation probability which can be adjusted automatically according to the diversity of population is also adopted. A local search method is used to explore the search space to accelerate the convergence towards global optimum. The performance of MDNA-GA in typical benchmark functions and kinetic parameter estimation is studied and compared with RNA-GA. The experimental results demonstrate that the proposed algorithm can overcome premature convergence and yield the global optimum with high efficiency.     

A quickly convergent continuous ant colony optimization algorithm with Scout Ants

Many studies on ants behavior have demonstrated that their food searching process starts with Scout Ants’ scouting all around for food. In this paper, we propose a novel Scout Ant Continuous Optimization (SACO) algorithm which can simulate the food searching process of the Scout Ants. In this algorithm, the solution space of an optimization problem is divided into m subspaces. One Scout Ant is assigned to each subspace, and a total number of m Scout Ants in m subspaces will cooperate in the whole food searching process. The location of a Scout Ant in a subspace corresponds to a solution in that subspace. When an ant moves, the change of its position is driven by two factors. The first factor is the independent, random ergodic search with a small moving step in the ant’s assigned subspace, and the second is the collaborative, global search inspired by the global heuristic information accumulated among m ants. Each of these two factors is weighed by an appropriate weight to balance its contribution to the moving step size. This balanced computation helps adapt optimization problems with different features. Our numerical experiments have demonstrated that, in addition to the high accuracy and success rate in seeking the optimized solutions, our algorithm also has very fast convergence speed and impressive performance in optimization applications in high-dimensional spaces.     

A variable neighborhood search based algorithm for finite-horizon Markov Decision Processes

This paper considers the application of a variable neighborhood search (VNS) algorithm for finite-horizon (H stages) Markov Decision Processes (MDPs), for the purpose of alleviating the “curse of dimensionality” phenomenon in searching for the global optimum. The main idea behind the VNSMDP algorithm is that, based on the result of the stage just considered, the search for the optimal solution (action) of state x in stage t is conducted systematically in variable neighborhood sets of the current action. Thus, the VNSMDP algorithm is capable of searching for the optimum within some subsets of the action space, rather than over the whole action set. Analysis on complexity and convergence attributes of the VNSMDP algorithm are conducted in the paper. It is shown by theoretical and computational analysis that, the VNSMDP algorithm succeeds in searching for the global optimum in an efficient way.     

A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this paper.The search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces,and we also proposed an adaptive rule for choosing different searching directions at each iteration.We obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent condition.With the used nonmonotone line search,we prove that the new algorithm is globally convergent for general nonlinear functions under some mild assumptions.Numerical experiments show that the proposed algorithm is promising for the given test problem set.     

Fast fractal image encoding using one-norm of normalised block

The baseline fractal image encoding with full search typically requires a very long encoding time, which is essentially spent on searching for the best-matched block to an input range block in a large domain pool. In this paper, one-norm of normalised block is first utilized to avoid the excessive search, in which the search process might be early terminated, and thus remaining domain blocks could be safely discarded. Then the encoding schemes proposed recently by the authors are used to further reduce the search space and improve the decoded image quality. Experiments show that, for three popular 512 × 512 test images, the proposed algorithm can averagely reduce the runtime by about 40 times while there is averagely the PSNR gain of 0.91 dB, in comparison with the baseline fractal algorithm. Besides, when combined with the kick-out condition and the zero contrast prediction proposed recently by Lai et al. [Lai CM, Lam KM, Siu WC. A fast fractal image coding based on kick-out and zero contrast conditions. IEEE Trans Image Process 2003:11;1398–403], the combined algorithm further reduced the runtime by about 10% while maintaining the same PSNR exactly as that of the proposed algorithm.