基于四次B样条的曲线逼近算法

考虑到插值算法增减节点困难,传统逼近算法精度不够等缺点,有文献提出一种基于三次B样条的曲线逼近算法。该算法通过迭代逼近,提高了计算速度与精度。在系统研究此算法的基础上,将该算法推广到四次B样条,使其具有三阶可导性,并给出该算法收敛性的理论证明。最后用该算法对常用函数进行逼近效果实验。结果表明,所提出的四次B样条的曲线逼近算法收敛速度更快,且能够满足更高精度的实际工业生产需要。     

模糊推理三I算法的连续性和逼近性

三I算法是一种新的模糊推理方法,可以作为传统的模糊推理方法的修改和补充.系统地研究了三I支持度算法和反向三I支持度算法的连续性问题,并指出了基于一些常用的蕴涵算子的三I算法具有逼近性.此结果对构建模糊控制系统和模糊专家系统时选用三I推理算法具有一定的指导作用.     

一种新的双步直线算法

本文提出一种新的直线逼近方法——类最佳逼近,基于这种逼近方法,斜率的直线和斜率为的直线具有互补的性质,利用这些性质,本文设计了一种新的双步直线方法,该算法揭示了直线计算的互补性,理论简单,精度达到最好。文章最后比较了该算法和传统的Brensenham算法,指出新算法大幅改善了Bresenham算法的计算能力。该算法对于硬件实现将更有益处。     

非线性小波变换在人脸识别中应用

文中提出非线性小波逼近在人脸识别中的应用，并将之与基于线性小波逼近的人脸识别算法进行比较，提出一种基于非线性小波逼近的高效算法，并且用实验证明本算法的具有很高识别率，同时证明本算法具有不会受到人脸姿态特征的影响的优点。     

星形网络的容错并行路由研究及概率分析

对具有错误结点的星形网络中的点与点之间的容错并行路由问题进行了研究,提出了一种新的具有容错能力的点对点的并行路由算法。严格证明了新算法的正确性,讨论了新算法的时间复杂度,并对新算法所找到的路径的长度进行了分析。用概率分析的方法对新算法的容错性概率进行了严格地推导,计算出概率的上下界。     

系统辨识与建模的一种新方法

本文从函数逼近观点研究线性和非线性系统辨 识问题,导出辨识方程,提出用神经网络建立线性和非线性系统的模型．根据函数内差逼近 原理建立神经网络学习方程,给出优化算法．计算机仿真表明新算法计算速度快,具有良好 的推广、逼近和收敛特性．     

一种基于线性函数逼近的离策略Q(λ)算法

将函数逼近用于强化学习是目前机器学习领域的一个新的研究热点.针对传统的基于查询表及函数逼近的Q(λ)学习算法在大规模状态空间中收敛速度慢或者无法收敛的问题,提出一种基于线性函数逼近的离策略Q(λ)算法.该算法通过引入重要性关联因子,在迭代次数逐步增长的过程中,使得在策略与离策略相统一,确保算法的收敛性.同时在保证在策略与离策略的样本数据一致性的前提下,对算法的收敛性给予理论证明.将文中提出的算法用于Baird反例、Mountain-Car及Random Walk仿真平台,实验结果表明,该算法与传统的基于函数逼近的离策略算法相比,具有较好的收敛性;与传统的基于查询表的算法相比,具有更快的收敛速度,且对于状态空间的增长具有较强的鲁棒性.     

无线传感网络节点隶属度模糊定位算法研究

无线传感网络RSSI算法理论上定位精度很高,但在实际工程应用中易受环境干扰和信号波动影响,无法精确,定位稳定性不好,误差较大,若穷追所有细节将极大增加算法复杂度;针对其中不足,提出基于节点隶属度模糊算法,并应用于RSSI定位模型之中;新算法首先利用岭形分布对RSSI初始信号模糊化取得隶属函数,再根据隶属度近似程度反馈求精,通过隶属程度以逐渐逼近方式定位节点坐标区域;新算法模糊化系数m值越大,抗干扰能力越强,但节点具有更多的相同或相似格贴近度,无法反映节点真实状态,因此m值应根据路径衰减指数和环境干扰物理值共同决定;通过仿真实验结果证明,与RSSI和DV-Hop算法相比,新算法可以减少环境干扰和信号波动带来的影响,具有更高的定位精度和稳定性。     

基于剪枝算法解决多处理机调度问题的算法研究

针对当前算法求解多处理机调度问题的不足,从剪枝策略的角度提出了一种笨人算法。笨人算法的思路是：不断排除最差解,直到剩下唯一解。这种剪枝算法至少保证当前的选择不是最差的,并且对计算过程的最大复杂度作了一个估计。经过实验分析,对于◢N×N◣的MSP,多数情况下,笨人算法比贪心算法、遗传算法、差分进化算法的表现更为稳定和优秀,是一种有效的算法,也为相关问题的研究提供了一种新的思路。     

一种改进的自适应遗传算法求解专家分配问题

基金项目管理中，专家分配问题的研究具有很现实的意义。在解决专家分配问题上做过一些基础性的工作，提出了使用遗传算法及一种信息素指导变异的新算法求解该问题。实验证明，遗传算法是一种可行的途径，并且信息素指导下的启发式变异操作，可以加速算法向最优解搜索。但是，这两种方法都存在局部搜索能力差的问题，在算法运行的中后期会出现大量的冗余迭代。鉴于此，提出一种信息素指导下的自适应变异方法求解专家分配问题。实验证明，新算法具有更强的收敛能力和局部搜索能力。     

Best position algorithms for efficient top-k query processing

The general problem of answering top-k queries can be modeled using lists of data items sorted by their local scores. The main algorithm proposed so far for answering top-k queries over sorted lists is the Threshold Algorithm (TA). However, TA may still incur a lot of useless accesses to the lists. In this paper, we propose two algorithms that are much more efficient than TA. First, we propose the best position algorithm (BPA). For any database instance (i.e. set of sorted lists), we prove that BPA stops as early as TA, and that its execution cost is never higher than TA. We show that there are databases over which BPA executes top-k queries O(m) times faster than that of TA, where m is the number of lists. We also show that the execution cost of our algorithm can be (m−1) times lower than that of TA. Second, we propose the BPA2 algorithm, which is much more efficient than BPA. We show that the number of accesses to the lists done by BPA2 can be about (m−1) times lower than that of BPA. We evaluated the performance of our algorithms through extensive experimental tests. The results show that over our test databases, BPA and BPA2 achieve significant performance gains in comparison with TA.     

New primal–dual algorithms for Steiner tree problems

We present new primal–dual algorithms for several network design problems. The problems considered are the generalized Steiner tree problem (GST), the directed Steiner tree problem (DST), and the set cover problem (SC) which is a subcase of DST. All our problems are NP-hard; so we are interested in their approximation algorithms. First, we give an algorithm for DST which is based on the traditional approach of designing primal–dual approximation algorithms. We show that the approximation factor of the algorithm is k, where k is the number of terminals, in the case when the problem is restricted to quasi-bipartite graphs. We also give pathologically bad examples for the algorithm performance. To overcome the problems exposed by the bad examples, we design a new framework for primal–dual algorithms which can be applied to all of our problems. The main feature of the new approach is that, unlike the traditional primal–dual algorithms, it keeps the dual solution in the interior of the dual feasible region. The new approach allows us to avoid including too many arcs in the solution, and thus achieves a smaller-cost solution. Our computational results show that the interior-point version of the primal–dual most of the time performs better than the original primal–dual method.     

A novel global harmony search algorithm for task assignment problem

The objective of task assignment problem (TAP) is to minimize the sum of interprocessor communication and task processing costs for a distributed system which subjects to several resource constraints. We use a novel global harmony search algorithm (NGHS) to solve this problem, and the NGHS algorithm has demonstrated higher efficiency than the improved harmony search algorithm (IHS) on finding the near optimal task assignment. We also devise a new method called normalized penalty function method to tradeo^® the costs and the constraints. A large number of experiments show that our algorithm performs well on finding the near optimal task assignment, and it is a viable approach for the task assignment problem.     

A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks

The hybrid flow-shop scheduling problem with multiprocessor tasks finds its applications in real-time machine-vision systems among others. Motivated by this application and the computational complexity of the problem, we propose a genetic algorithm in this paper. We first describe the implementation details, which include a new crossover operator. We then perform a preliminary test to set the best values of the control parameters, namely the population size, crossover rate and mutation rate. Next, given these values, we carry out an extensive computational experiment to evaluate the performance of four versions of the proposed genetic algorithm in terms of the percentage deviation of the solution from the lower bound value. The results of the experiments demonstrate that the genetic algorithm performs the best when the new crossover operator is used along with the insertion mutation. This genetic algorithm also outperforms the tabu search algorithm proposed in the literature for the same problem.     

Intelligent multi-user detection using an artificial immune system

Artificial immune systems (AIS) are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multiple-access communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.     

一种基于新型优化算法的多播路由选择方法

遗传算法、蚁群优化算法已在多播路由优化问题中得到了广泛应用,但由于算法本身的缺陷,二者在具体应用时都存在着时间性能与优化性能之间的矛盾。论文将遗传算法与蚁群优化算法二者合成,优势互补。仿真实验表明,应用这种算法于多播路由问题,可以得到比现有启发式算法更好的结果。     

Bit-Parallel Witnesses and Their Applications to Approximate String Matching

We present a new bit-parallel technique for approximate string matching. We build on two previous techniques. The first one, BPM (Myers, 1999), searches for a pattern of length m in a text of length n permitting k differences in $O(\lceil m/w \rceil n)$ time, where w is the width of the computer word. The second one, ABNDM (Navarro and Raffinot, 2000), extends a sublinear-time exact algorithm to approximate searching. ABNDM relies on another algorithm, BPA (Wu and Manber, 1992), which makes use of an $O(k \lceil m/w \rceil n)$ time algorithm for its internal workings. BPA is slow but flexible enough to support all operations required by ABNDM. We improve previous ABNDM analyses, showing that it is average-optimal in number of inspected characters, although the overall complexity is higher because of the $O(k \lceil m/w \rceil )$ work done per inspected character. We then show that the faster BPM can be adapted to support all the operations required by ABNDM. This involves extending it to compute edit distance, to search for any pattern suffix, and to detect in advance the impossibility of a later match. The solution to those challenges is based on the concept of a witness, which permits sampling some dynamic programming matrix values to bound, deduce or compute others fast. The resulting algorithm is average-optimal for m ≤ w, assuming the alphabet size is constant. In practice, it performs better than the original ABNDM and is the fastest algorithm for several combinations of m, k and alphabet sizes that are useful, for example, in natural language searching and computational biology. To show that the concept of witnesses can be used in further scenarios, we also improve a recent variant of BPM. The use of witnesses greatly improves the running time of this algorithm too.     

Comparing expressibility of normed BPA and normed BPP processes

We present an exact characterization of those transition systems which can be equivalently (up to bisimilarity) defined by the syntax of normed BPA and normed BPP processes. We give such a characterization for the subclasses of normed BPA and normed BPP processes as well. Next we demonstrate the decidability of the problem whether for a given normed BPA process there is some unspecified normed BPP process such that and are bisimilar. The algorithm is polynomial. Furthermore, we show that if the answer to the previous question is positive, then (an example of) the process is effectively constructible. Analogous algorithms are provided for normed BPP processes. Simplified versions of the mentioned algorithms which work for normed BPA and normed BPP are given too. As a simple consequence we obtain the decidability of bisimilarity in the union of normed BPA and normed BPP processes. Received: 3 June 1997     

Fleet-sizing for multi-depot and periodic vehicle routing problems using a modular heuristic algorithm

In this paper, we address the problem of determining the optimal fleet size for three vehicle routing problems, i.e., multi-depot VRP, periodic VRP and multi-depot periodic VRP. In each of these problems, we consider three kinds of constraints that are often found in reality, i.e., vehicle capacity, route duration and budget constraints. To tackle the problems, we propose a new Modular Heuristic Algorithm (MHA) whose exploration and exploitation strategies enable the algorithm to produce promising results. Extensive computational experiments show that MHA performs impressively well, in terms of solution quality and computational time, for the three problem classes.     

Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm (MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the large-sized problems.