双混沌机制优化方法及其应用

为了克服混沌优化方法在缩小优化变量的搜索空间前所进行的盲目搜索，提出一种具有双混沌机制的优化方法。该方法同时利用两种不同的混沌机制在搜索空间进行搜索，根据搜索情况来缩小搜索空间。该方法能够改善算法的通用性，避免了针对不同的优化函数选择搜索参数的缺点。仿真结果表明，该方法的搜索效率明显高于普通的混沌优化方法。     

基于P2P的资源搜索方法研究

基于Peer-tO-Peer(P2P)理念的搜索方法较传统搜索方法具备更强的实时性和有效性，为新一代搜索引擎的研究提供了新的思路。文中首先介绍了基于F2P搜索方法的特点及分类，描述了各类基于F2P的资源搜索方法。在此基础上，分析了各类搜索方法的优缺点，并阐述了各类搜索方法的改进思路。最后探讨了基于F2P的资源搜索方法的研究与发展方向。     

通用搜索算法的设计

问题求解系统常常涉及到多种形式的搜索方法，搜索方法的关键是搜索算法设计。这里给出一个简捷的通用搜索算法，算法采用结点指针和搜索指针对关系矩阵进行双重处理的方法，搜索过程中不断改变结点指针和搜索指针，进行推进和回退处理，最终找出问题的全部解。     

能力风暴机器人AS-UⅡ目标搜索研究

能力风暴机器人AS-UⅡ一般是通过一种或者两种自身携带的传感器实现目标搜索，但目标位置确定比较粗糙。通过综合应用多种传感器，采用粗搜索和精搜索相结合的方法，建立随机漫游轨迹算法实现粗搜索，建立方向和位置误差算法以实现精搜索，最后设计了目标搜索程序进行了模拟仿真和实际应用试验。结果表明：搜索目标可行，目标位置确定准确，其误差与一般搜索方法相比，大大降低，达到满意效果。     

JaVa Bean构件搜索引擎

现有的诸多搜索引擎，由于使用关键字搜索方法，对于专业化的构件搜索效率不高。针对该问题，该文提出一种在Internet环境下JavaBean构件的搜索方法。原型系统表明，利用这种方法构造的JavaBean构件搜索引擎具有很高的搜索效率。     

一种新的搜索方法

在分析二分搜索方法基础上，提出了一种新的搜索方法。通过测试，该方法简单且比二分搜索方法效率还高。     

复杂条件下的社区搜索方法

社区搜索旨在寻找包含给定节点集的社区，能够快速获取个性化的社区信息.针对现有社区搜索算法难以满足复杂搜索条件的现状，提出条件社区搜索这一新问题.解决该问题有助于对社交网络进行智能分析，在复杂搜索条件下为用户提供更好的社区结果.首先，基于布尔表达式，给出条件社区搜索问题的形式化定义，可有效表达给定节点不能出现在社区内以及给定节点中至少有一个出现在社区内的要求.接着，提出解决条件社区搜索问题的通用框架，包括对搜索条件进行简化、根据简化后的搜索条件进行多次单项条件社区搜索、合并各单项条件社区搜索的结果等主要步骤.同时，提出"社区搜索+过滤"的方法和给点加权的方法来进行单项条件社区搜索.最后，真实数据集上的大量实验结果表明所提方法的正确性和有效性.     

踩点式信息搜索的新方法

从小偷踩点获取藏金信息中受到启发，提出了一种互联网信息智能搜索新方法。能够从已经分好类的特定领域网站中，准确高效地搜索出隐藏于其内部的目标网页。把所有的搜索网页根据检索信息分成两类：一类是信息点，一类是信息路径。采用信息路径特征与信息点信息量特征描述有机结合而形成的一种新的搜索知识表示方法。基于这种知识表示方法，智能搜索方法不仅能够对网站中网页进行深度优先的智能搜索，而且还能够通过对其搜索过程和结果的自学习来获取更多更好的搜索知识。     

动态图象运动矢量多重跟踪搜索算法及实现

针对动态图象运动矢量搜索过程中，使用普通的对数搜索方法有可能无法搜索到真实最优运动矢量的问题，分析了产生该问题的原因，并提出使用运动矢量多重跟踪方法进行运动矢量搜索。在图象匹配的过程中应用亚采样模板有效地降低了该方法的计算量。对大量数据的分析试验证实，该方法具有很好地适应多极点匹配图象的特点，能准确搜索到真实最优运动矢量点。     

分形图象编码中的快速插索方法研究

在深入研究分形图象编码原理的基础上，提出了一种基于“区域块与简单变换后的范围块之间有差关系的快速搜索方法－－Ｒ－Ｄ搜索方法。与ＪＦＢ搜索方法相比，在重建图象质量的条件下，Ｒ－Ｄ搜索方法使编码速度大约提高２倍。     

A Monte-Carlo approach for 0–1 programming problems

A two-phase global random search procedure for solving some computationally intractable discrete optimization problems is proposed. Guarantees for quality of random search results are derived from analysis of non-asymptotic order statistics and distribution-free intervals that are obtainable in this way: the confidence interval for a quantile of given order, or the tolerance interval for the parent distribution of goal function values. It has been shown that results, related to the multiconstrained 0–1 knapsack problem, within a few percentage from the true optimal solution can be obtained.     

Combining SAT Methods with Non-Clausal Decision Heuristics

A decision procedure for arbitrary first-order formulas can be viewed as combining a propositional search with a decision procedure for conjunctions of first-order literals, so Boolean SAT methods can be used for the propositional search in order to improve the performance of the overall decision procedure. We show how to combine some Boolean SAT methods with non-clausal heuristics developed for first-order decision procedures. The combination of methods leads to a smaller number of decisions than either method alone.     

最小化空闲时间的Job-shop调度进化算法

为了研究Job-shop调度问题,分析了调度结果和调度过程,认为传统Job-shop调度模型的调度过程,实质是减少并减小空闲时间的组合优化过程,而且不同空闲时间对调度结果的影响程度不同。据此提出了最小化空闲时间的两个处理过程和不同空闲时间的处理顺序规则;并设计了进化算法中最小化空闲时间的初始种群生成过程、重组算子和变异算子。经典的调度基准问题对比测试表明最小化空闲时间的分析结论是正确的;最小化空闲时间过程高效可靠;最小化空闲时间的进化算法缩小了算法的搜索空间,大大提高了搜索效率,有效避免了早熟收敛现象,稳定可靠。     

Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem

We consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop three heuristic approaches, i.e., a genetic algorithm, a tabu search algorithm and a hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solutions. The branch-and-bound procedure will check whether the solutions obtained by the meta-heuristics can be scheduled within a tight upper bound. We compare the performances of these heuristics on a standard dataset available in the literature. Moreover, the influence of the different heuristic parameters is examined as well. The computational experiments reveal that the hybrid heuristics are able to compete with the best known results from the literature.     

A hybrid Electromagnetism-like Mechanism/tabu search procedure for the single machine scheduling problem with a maximum lateness objective

This paper presents a hybrid meta-heuristic search procedure to solve the well-known single machine scheduling problem to minimize the maximum lateness over all jobs, where precedence relations may exist between some of the jobs. The hybridization consists of a well-designed balance between the principles borrowed from an Electromagnetism-like Mechanism algorithm and the characteristics used in a tabu search procedure. The Electromagnetism-like Mechanism (EM) algorithm follows a search pattern based on the theory of physics to simulate attraction and repulsion of solutions in order to move towards more promising solutions. The well-known tabu search enhances the performance of a local search method by using memory structures by prohibiting visited solutions during a certain time of the search process. The hybridization of both algorithms results in an important trade-off between intensification and diversification strategies. These strategies will be discussed in detail. To that purpose, a new set of data instances is used to compare different elements of the hybrid search procedure and to validate the performance of the algorithm.     

Integrated optimization of inventory-distribution systems by random local search and a genetic algorithm

A new model and its solution procedure for the commodity distribution system consisting of distribution centers and consumer points are discussed. Demand is assumed to be a random variable that obeys a known, stationary probability distribution. An integrated optimization model is built where both the order-up-to-R policy, which is one of the typical inventory policies for periodic review models, and the transportation problem are considered simultaneously. The assignment of consumer points to distribution centers is not fixed. The problem is to determine the target inventory and the transportation quantity in order to minimize the expectation of the sum of inventory related costs and transportation costs. Simulation and linear programming are used to calculate the expected costs, and a random local search method is developed in order to determine the optimum target inventory. A genetic algorithm is also tested and compared with the proposed random local search method. The model and effectiveness of the proposed solution procedure are clarified by computational experiments.     

Optimum design of composite laminates for minimum thickness

In this study, an optimization procedure is proposed to minimize thickness (or weight) of laminated composite plates subject to in-plane loading. Fiber orientation angles and layer thickness are chosen as design variables. Direct search simulated annealing (DSA), which is a reliable global search algorithm, is used to search the optimal design. Static failure criteria are used to determine whether load bearing capacity is exceeded for a configuration generated during the optimization process. In order to avoid spurious optimal designs, both the Tsai–Wu and the maximum stress criteria are employed to check static failure. Numerical results are obtained and presented for different loading cases.     

改进蜂群算法的图像阈值分割方法

为快速高效地进行图像分割,针对人工蜂群算法存在的收敛速度慢、易陷入局部最优解等问题,提出了一种基于改进人工蜂群算法分割二维OTSU图像的新方法。通过对蜜源更新过程中向当前最优蜜源方向进行引导,可以加快算法的收敛速度;为避免算法陷入局部最优并加快收敛速度,在局部搜索过程中逐步缩减了搜索范围并加入了放弃机制;针对较大梯度值无意义的问题,限定了蜜源范围,以提高算法的效率。最后结合具有不同直方图分布的图像进行了实验,结果表明了算法稳健、高效、快速的特性。     

Parameter optimization in large-scale dynamical systems: a method of contractive mapping

A new method is given to optimize parameters in dynamical systems by supplementing conventional methods with a procedure of contractive mapping. The dynamical system is first decoupled in order to reduce the search size. The reference curves and the simulation curves are obtained with the pre-optimization. A contractive mapping procedure is designed to asymptotically reduce the differences between the curves, whereby the parameters are further optimized. Numerical results indicate that the present method can solve some large-scale problems that are difficult for the previous methods.     

Attention-based dynamic visual search using inner-scene similarity: algorithms and bounds

A visual search is required when applying a recognition process on a scene containing multiple objects. In such cases, we would like to avoid an exhaustive sequential search. This work proposes a dynamic visual search framework based mainly on inner-scene similarity. Given a number of candidates (e.g., subimages), we hypothesize is that more visually similar candidates are more likely to have the same identity. We use this assumption for determining the order of attention. Both deterministic and stochastic approaches, relying on this hypothesis, are considered. Under the deterministic approach, we suggest a measure similar to Kolmogorov's epsilon-covering that quantifies the difficulty of a search task. We show that this measure bounds the performance of all search algorithms and suggest a simple algorithm that meets this bound. Under the stochastic approach, we model the identity of the candidates as a set of correlated random variables and derive a search procedure based on linear estimation. Several experiments are presented in which the statistical characteristics, search algorithm, and bound are evaluated and verified.