UNCERTAIN OPTIMAL CONTROL WITH APPLICATION TO A PORTFOLIO SELECTION MODEL

Optimal control is a very important field of study not only in theory but in applications, and stochastic optimal control is also a significant branch of research in theory and applications. Based on the concept of uncertain process, an uncertain optimal control problem is dealt with. Applying Bellman's principle of optimality, the principle of optimality for uncertain optimal control is obtained, and then a fundamental result called the equation of optimality in uncertain optimal control is given. Finally, as an application, the equation of optimality is used to solve a portfolio selection model.     

“Super-overtaking” optimal policies for Markov control processes

In this paper we study the average cost optimization problem for discrete-time Markov control processes on Borel space with possibly unbounded costs. Under proper ergodicity assumptions we show the existence of stationary policies for which asymptotic (as N → ∞) behavior of some functions of N-horizon costs can be better than for overtaking optimal policies.     

“人口最优控制”的一点注记

本文给出时间连续按龄离散的人口发展模型最优性问题的充分条件,     

Novel Cluster Validity Index for FCM Algorithm

How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means （FCM）. In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.     

Hybrid strategy for selecting compact set of clustering partitions

The selection of the most appropriate clustering algorithm is not a straightforward task, given that there is no clustering algorithm capable of determining the actual groups present in any dataset. A potential solution is to use different clustering algorithms to produce a set of partitions (solutions) and then select the best partition produced according to a specified validation measure; these measures are generally biased toward one or more clustering algorithms. Nevertheless, in several real cases, it is important to have more than one solution as the output. To address these problems, we present a hybrid partition selection algorithm, HSS, which accepts as input a set of base partitions potentially generated from clustering algorithms with different biases and aims, to return a reduced and yet diverse set of partitions (solutions). HSS comprises three steps: (i) the application of a multiobjective algorithm to a set of base partitions to generate a Pareto Front (PF) approximation; (ii) the division of the solutions from the PF approximation into a certain number of regions; and (iii) the selection of a solution per region by applying the Adjusted Rand Index. We compare the results of our algorithm with those of another selection strategy, ASA. Furthermore, we test HSS as a post-processing tool for two clustering algorithms based on multiobjective evolutionary computing: MOCK and MOCLE. The experiments revealed the effectiveness of HSS in selecting a reduced number of partitions while maintaining their quality.     

Traditional vs. extended optimality in topology optimization

Owing to its implications with respect to a critical examination of the SIMP and ESO methods in a Forum Article, extended optimality in topology optimization is revisited, with a view to clarifying certain issues and to illustrate this concept with a case study. It is concluded that extended optimality can result in a much lower structural volume than traditional optimality.     

Analogue of the Kelley condition for optimal systems with retarded control

In this paper, we consider an optimal control problem with retarded control and study a larger class of singular (in the classical sense) controls. The Kelley and equality type optimality conditions are obtained. To prove our main results, we use the Legendre polynomials as variations of control.     

一个基于复制的相关任务调度算法

基于任务复制的调度是一种新的调度方法，现已有许多基于任务复制的调度算法在任务满足某些条件时能产生最优调度，但也存在一些不足．因此，针对一些算法存在的问题，提出一种新调度算法，该算法既考虑合并其它父任务以减少通讯时间，同时尽可能少的合并祖先任务，从而尽量减小任务的启动时间，因而能产生更短的调度．大量实验数据表明，该算法的性能明显优于其它算法。     

Comparing the performance of metaheuristics for the analysis of multi-stakeholder tradeoffs in requirements optimisation

Context

In requirements engineering, there will be many different stake holders. Often the requirements engineer has to find a set of requirements that reflect the needs of several different stake holders, while remaining within budget.

Objective

This paper introduces an optimisation-based approach to the automated analysis of requirements assignments when multiple stake holders are to be satisfied by a single choice of requirements.

Method

The paper reports on experiments using two different multi-objective evolutionary optimisation algorithms with real world data sets as well as synthetic data sets. This empirical validation includes a statistical analysis of the performance of the two algorithms.

Results

The results reveal that the Two-Archive algorithm outperformed the others in convergence as the scale of problems increase. The paper also shows how both traditional and animated Kiviat diagrams can be used to visualise the tensions between the stake holders’ competing requirements in the presence of increasing budgetary pressure.

Conclusion

This paper presented the concept of internal tensioning among multi-stakeholder in requirements analysis and optimisation for the first time. This analysis may be useful in internal negotiations over budgetary allowance for the project.     

动态规划算法原理及应用研究

本文透彻分析了动态规划算法的基本原理、基本要素、适用条件和解题步骤。并通过达尔文芯片问题详细讲解了动态规划算法在实际中的运用,最后总结了使用动态规划算法的重点难点和技巧。     

AGAINST OPTIMALITY: LOGICAL FOUNDATIONS FOR DECISION-THEORETIC PLANNING IN AUTONOMOUS AGENTS

This paper investigates decision-theoretic planning in sophisticated autonomous agents operating in environments of real-world complexity. An example might be a planetary rover exploring a largely unknown planet. It is argued that existing algorithms for decision-theoretic planning are based on a logically incorrect theory of rational decision making. Plans cannot be evaluated directly in terms of their expected values, because plans can be of different scopes, and they can interact with other previously adopted plans. Furthermore, in the real world, the search for optimal plans is completely intractable. An alternative theory of rational decision making is proposed, called "locally global planning."     

Strong 1-optimal stationary policies in denumerable Markov decision processes

Usual conditions for existence of stationary average optimal policies in denumerable MDPs with general bounded rewards are shown to be also sufficient for strong 1-optimality. Moreover, we prove that all limit points of discounted optimal stationary policies when the discount factor goes to 1 are strong 1-optimal.     

Markov decision processes with iterated coherent risk measures

This paper considers a Markov decision process in Borel state and action spaces with the aggregated (or say iterated) coherent risk measure to be minimised. For this problem, we establish the Bellman optimality equation as well as the value and policy iteration algorithms, and show the existence of a deterministic stationary optimal policy. The cost function, while being allowed to be unbounded from below (in the sense that its negative part needs be bounded by some nonnegative real-valued possibly unbounded weight function), can be arbitrarily unbounded from above and possibly infinitely valued.     

Optimal control for multi-stage and continuous-time linear singular systems

In this paper, optimal control problems for multi-stage and continuous-time linear singular systems are both considered. The singular systems are assumed to be regular and impulse-free. First, a recurrence equation is derived according to Bellman's principle of optimality in dynamic programming. Then, by applying the recurrence equation, bang-bang optimal controls for the control problems with linear objective functions subject to two types of multi-stage singular systems are obtained. Second, employing the principle of optimality, a equation of optimality for settling the optimal control problem subject to a class of continuous-time singular systems is proposed. The optimal control problem may become simpler through solving this equation of optimality. Two numerical examples and a dynamic input–output model are presented to show the effectiveness of the results obtained.     

非对称信息下供应链库存系统Pareto优化模型

运用委托代理理论,研究了非对称信患条件下,由单供应商单分销商组成的供应链Pareto优化问题.在假定分销商所面临的需求是与销售价格有关的随机变量,以及供、销双方关于分销商销售价格信息不对称这两个前提下,将供应商作为委托人,分销商作为代理人,给出了供应商为吸引销售商选择对自己最有利的销售价格而设计的最优激励合同,比较了在不同信息条件下供销双方的最优决策.最后,给出了应用实例和灵敏度分析.     

一种并行的信息系统属性约简算法*

属性约简是信息系统中的一个重要操作,而分类是属性约简的基础,且直接在大数据集上进行属性约简往往存在效率低下的问题。故以分类为基础提出了一种基于信息熵的信息系统并行属性约简算法。该算法通过信息熵的计算,在属性约简的同时对原信息系统逐层分解成尽量均匀的子表,从而实现了属性约简的并行计算并缩小了搜索空间。对该算法的时间复杂度进行了分析,实验表明,该算法在效率方面优于传统算法。     

一种高效的多目标演化算法

为了提高非劣解向Pareto最优前沿收敛的速度及进一步提高解的精度,在设计了一种新的杂交算子并改进了NSGA-Ⅱ的拥挤操作的基础上,提出了一种基于分级策略的多目标演化算法。数值实验表明,新算法能够非常高效地处理高维的最优前沿为凸的、非凸的和不连续前沿的多目标测试函数,得到的非劣解具有很好的分布性质。但在处理高维的具有太多局部最优前沿的多峰函数时极易陷入局部最优前沿。     

On overtaking optimal tracking for linear systems

Optimal tracking problems involving a time-varying linear plant and infinite-horizon quadratic performance index are considered. In general, such problems yield an unbounded performance index for every control and thus must be interpreted as so-called overtaking optimal control problems. A completing-the-square argument is used to derive the overtaking optimal control for a general problem formulation, and various properties are discussed.     

A theory of global concurrency control in multidatabase systems

This article presents a theoretical basis for global concurrency control to maintain global serializability in multidatabase systems. Three correctness criteria are formulated that utilize the intrinsic characteristics of global transactions to determine the serialization order of global subtransactions at each local site. In particular, two new types of serializability, chain-conflicting serializability and sharing serializability, are proposed and hybrid serializability, which combines these two basic criteria, is discussed. These criteria offer the advantage of imposing no restrictions on local sites other than local serializability while retaining global serializability. The graph testing techniques of the three criteria are provided as guidance for global transaction scheduling. In addition, an optimal property of global transactions for determinating the serialization order of global subtransactions at local sites is formulated. This property defines the upper limit on global serializability in multidatabase systems.     

Automated Discovery of Numerical Approximation Formulae via Genetic Programming

This paper describes the use of genetic programming to perform automated discovery of numerical approximation formulae. We present results involving rediscovery of known approximations for Harmonic numbers, discovery of rational polynomial approximations for functions of one or more variables, and refinement of existing approximations through both approximation of their error function and incorporation of the approximation as a program tree in the initial GP population. Evolved rational polynomial approximations are compared to Padé approximations obtained through the Maple symbolic mathematics package. We find that approximations evolved by GP can be superior to Padé approximations given certain tradeoffs between approximation cost and accuracy, and that GP is able to evolve approximations in circumstances where the Padé approximation technique cannot be applied. We conclude that genetic programming is a powerful and effective approach that complements but does not replace existing techniques from numerical analysis.