融合遗传蚁群算法的区域覆盖卫星星座优化算法

针对特定区域覆盖并密集重访的卫星星座优化设计问题,采用回归轨道和共星下点轨迹星座的设计方案,提出特定区域内重点地区权值排序覆盖并融合遗传蚁群算法优化求解卫星星座轨道参数的方法。分析区域覆盖星座的设计需求,建立回归轨道覆盖区域模型,利用遗传蚁群算法计算出最优轨道根数,使用共星下点轨迹星座求解算法求出所有星座参数。仿真实验结果表明优化设计的星座满足对于区域目标的覆盖时间和重访次数需求,并对重要地点按照权值排序进行了侧重性覆盖和重访,验证了算法的可行性。     

Screening and selection procedures with control variates and correlation induction techniques

We consider the problem of identifying the simulated system with the best expected performance measure when the number of alternatives is finite and small (often < 500). Recently, more research efforts in the simulation community have been directed to develop ranking and selection (R&S) procedures capable of exploiting variance reduction techniques (especially the control variates). In this article, we propose new R&S procedures that can jointly use control variates and correlation induction techniques (including antithetic variates and Latin hypercube sampling). Empirical results and a realistic illustration show that the proposed procedures outperform the conventional procedures using sample means or control variates alone. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

Minimum violations and predictive meta‐rankings for college football

This article presents two meta‐ranking models that minimize or nearly minimize violations of past game results while predicting future game winners as well as or better than leading current systems—a combination never before offered for college football. Key to both is the development and integration of a highly predictive ensemble probability model generated from the analysis of 36 existing college football ranking systems. This ensemble model is used to determine a target ranking that is used in two versions of a hierarchical multiobjective mixed binary integer linear program (MOMBILP). When compared to 75 other systems out‐of‐sample, one MOMBILP was the leading predictive system while getting within 0.64% of the retrodictive optimum; the other MOMBILP minimized violations while achieving a prediction total that was 2.55% lower than the best mark. For bowls, prediction sums were not statistically significantly different from the leading value, while achieving optimum or near‐optimum violation counts. This performance points to these models as potential means of reconciling the contrasting perspectives of predictiveness versus the matching of past performance when it comes to ranking fairness in college football. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 17–33, 2014     

基于话题模型的专家发现方法

专家发现是实体检索的一个重要方面。经典的专家发现模型建立在专家与词项的条件独立性假设基础上。在实际应用中该假设通常不成立,使得专家发现的效果不够理想。本文提出了一种基于话题模型的专家发现方法,该方法无需依赖候选专家与词项的条件独立性假设,且其可操作性比经典模型更强。同时,使用了一种排序截断技术,该技术极大地降低了模型的计算复杂度。使用CERC(CSIRO Enterprise Research Collection)数据集对模型的性能进行评估。实验结果表明,基于话题模型的专家发现方法在各个评价指标上均优于经典的专家发现模型,能够有效地提高专家发现的效能。     

基于用户相关反馈的排序学习算法研究

在信息检索中,系统需要根据用户查询将文档按照相似度大小进行排序,吸引了众多信息检索和机器学习领域研究者的眼球,并形成了诸多排序算法模型。然而并未考虑到查询短语与文档构成的特征对与用户相关反馈之间存在的同质性。在机器学习算法基础上,通过提取训练样本的主要特征进行有效聚类,并结合用户的相关反馈获取各个类中相关度判断的置信值,形成相似度判定模型,应用该模型来对测试样本进行相关度排序。算法对LETOR数据集进行了测试,实验表明,信息检索性能指标比其他排序算法有了进一步提高,并且无需复杂的数据预处理工作和手动设定算法参数。     

基于熵权多目标决策的雷达网战损装备抢修排序方法

分析了熵权的理论基础，给出了一种基于熵权多目标决策的雷达网战损装备抢修排序方法。其中，采用熵权与决策者的主观权重相结合的方法确定综合权重，使权重的确定更加合理。最后通过实例对该方法的可行性和实用性进行了说明。     

Estimating the probability that a simulated system will be the best

Consider a stochastic simulation experiment consisting of v independent vector replications consisting of an observation from each of k independent systems. Typical system comparisons are based on mean (long‐run) performance. However, the probability that a system will actually be the best is sometimes more relevant, and can provide a very different perspective than the systems' means. Empirically, we select one system as the best performer (i.e., it wins) on each replication. Each system has an unknown constant probability of winning on any replication and the numbers of wins for the individual systems follow a multinomial distribution. Procedures exist for selecting the system with the largest probability of being the best. This paper addresses the companion problem of estimating the probability that each system will be the best. The maximum likelihood estimators (MLEs) of the multinomial cell probabilities for a set of v vector replications across k systems are well known. We use these same v vector replications to form v^k unique vectors (termed pseudo‐replications) that contain one observation from each system and develop estimators based on AVC (All Vector Comparisons). In other words, we compare every observation from each system with every combination of observations from the remaining systems and note the best performer in each pseudo‐replication. AVC provides lower variance estimators of the probability that each system will be the best than the MLEs. We also derive confidence intervals for the AVC point estimators, present a portion of an extensive empirical evaluation and provide a realistic example. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 341–358, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10019     

Fully sequential procedures for comparing constrained systems via simulation

We consider the problem of finding the system with the best primary performance measure among a finite number of simulated systems in the presence of a stochastic constraint on a single real‐valued secondary performance measure. Solving this problem requires the identification and removal from consideration of infeasible systems (Phase I) and of systems whose primary performance measure is dominated by that of other feasible systems (Phase II). We use indifference zones in both phases and consider two approaches, namely, carrying out Phases I and II sequentially and carrying out Phases I and II simultaneously, and we provide specific example procedures of each type. We present theoretical results guaranteeing that our approaches (general and specific, sequential and simultaneous) yield the best system with at least a prespecified probability, and we provide a portion of an extensive numerical study aimed at evaluating and comparing the performance of our approaches. The experimental results show that both new procedures are useful for constrained ranking and selection, with neither procedure showing uniform superiority over the other.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

基于个体判断优先序的多目标群决策方法研究

针对多目标群决策问题,深入分析了群意见的形成方式.对基于方案优先序的多目标群决策问题给出了若干求解群排序的方法.对其求解原理和集结结果进行了对比,并分析了各方法的实质及其等价关系;澄清了一些模糊认识,指出了某些方法存在的问题,对此类多目标群决策问题的求解提供了依据.