Optimal Control Problem for Risk‐Sensitive Mean‐Field Stochastic Delay Differential Equation with Partial Information

This paper deals with the risk‐sensitive control problem for mean‐field stochastic delay differential equations (MF‐SDDEs) with partial information. Firstly, under the assumptions that the control domain is not convex and the value function is non‐smooth, we establish a stochastic maximum principle (SMP). Then, by means of Itô's formula and some continuous dependence, we prove the existence and uniqueness results for another type of MF‐SDDEs. Meanwhile, the verification theorem for the MF‐SDDEs is obtained by using a clever construction of the Hamiltonian function. Finally, based on our verification theorem, a linear‐quadratic system is investigated and the optimal control is also derived by the stochastic filtering technique.     

宽度优先反复加宽及其启发式搜索算法研究

首先针对搜索树中深度固定且目标唯一的寻优问题，指出宽度优先反复加宽的搜索效率要比深度优先反复加深的搜索效率高，基于此，提出了基于宽度优先反复加宽的启发式搜索算法ＩＷＡ＊，算法ＩＷＡ＊是可采纳的。为了保持算法ＩＷＡ＊的搜索效率高于算法ＩＤＡ＊的搜索效率，同时又使算法ＩＷＡ＊的存贮空间复杂度减低，文中基于分层技术，提出了基于深度优先的ＩＷＡ＊算法──ＩＤＷＡ＊。算法ＩＤＷＡ＊也是一个可采纳的启发式搜索算法。     

噪声图象中提取边界的随机启发式搜索方法

在噪声图象中如何有效地提取边界是图象分析领域中的难点。启发式搜索的方法常常用于提取边界,但是,这种方法由于采用固定的起始点、固定的引导度量以及对图象仅进行一次性搜索,对噪声往往很敏感,为此提出了一种随机启发式搜索算法,该方法随机地选取起始点,并依照引导度量的概率反复地进行随机搜索获得各种可能的边界轨迹,然后进行各搜索轨迹的积累自增强,最后根据自增强积累统计结果获得边界。大量的实验结果证明,在噪声图象中,随机启发式搜索方法可以在提取出有意义边界的同时有效地抑制噪声。     

Timetabling through Constrained Heuristic Search and Genetic Algorithms

Hybrid genetic algorithms are presented that use constrained heuristic search and genetic techniques for the timetabling problem (TP). The TP is an NP-hard problem for which a general polynomial time deterministic algorithm is not known. The paper describes the classification of constraints and the constraint ordering to obtain the minimization of backtracking and the maximization of parallelism. The school timetabling problem is discussed in detail as a case study. The genetic algorithm approach is particularly well suited to this kind of problem, since there exists an easy way to assess a good timetable, but not a well structured automatic technique for constructing it. So, a population of timetables is created that evolves toward the best solution. The evaluation function and the genetic operators are well separated from the domain-specific parts, such as the knowledge of the problem and the heuristics, i.e. from the timetable builder. The present paper illustrates an approach based on the hybridization of constrained heuristic search with novel genetic algorithm techniques. It compares favourably with known programs to solve decision problems under logic constraints. The cost of the new algorithm and the quality of the solutions obtained in significant experiments are reported.     

Conditional value‐at‐risk beyond finance: a survey

A large number of problems involve making decisions in an uncertain environment and, hence, with unknown outcomes. Optimization models aimed at controlling the trade‐off between risk and return in finance have been widely studied since the seminal work by Markowitz in 1952. In financial applications, shortfall or quantile risk measures are receiving ever‐increasing attention. Conditional value‐at‐risk (CVaR) is arguably the most popular of such measures. In the last decades, optimization models aimed at controlling risk have been applied to several application domains different from financial optimization. This survey provides an overview of the main contributions where CVaR is incorporated into an optimization approach and applied to a context different from financial engineering. The literature is classified following an application‐oriented perspective. The applications cover classical areas studied in operational research—such as supply chain management, scheduling, and networks—and less classical areas such as energy and medicine. For each area, concise paper excerpts are provided that convey the main ideas of the problems studied, and analyze how the CVaR has been used to cope with different sources of uncertainty. Finally, some open research directions are outlined.     

用两阶段启发式算法编制连铸生产计划

现代钢铁企业一个最具挑战性的问题就是如何充分使用成本高昂的设备,以换取更高的利润。编制高效的连铸生产计划在提高生产率和降低能耗方面具有十分重要的意义。提出描述连铸计划的数学模型,并使用智能搜索算法解决这类ＮＰ难的问题     

Local Search Algorithms for SAT: An Empirical Evaluation

Local search algorithms are among the standard methods for solving hard combinatorial problems from various areas of artificial intelligence and operations research. For SAT, some of the most successful and powerful algorithms are based on stochastic local search, and in the past 10 years a large number of such algorithms have been proposed and investigated. In this article, we focus on two particularly well-known families of local search algorithms for SAT, the GSAT and WalkSAT architectures. We present a detailed comparative analysis of these algorithms" performance using a benchmark set that contains instances from randomized distributions as well as SAT-encoded problems from various domains. We also investigate the robustness of the observed performance characteristics as algorithm-dependent and problem-dependent parameters are changed. Our empirical analysis gives a very detailed picture of the algorithms" performance for various domains of SAT problems; it also reveals a fundamental weakness in some of the best-performing algorithms and shows how this can be overcome.     

Stochastic Point‐to‐Point Iterative Learning Control Based on Stochastic Approximation

An iterative learning control algorithm with iteration decreasing gain is proposed for stochastic point‐to‐point tracking systems. The almost sure convergence and asymptotic properties of the proposed recursive algorithm are strictly proved. The selection of learning gain matrix is given. An illustrative example shows the effectiveness and asymptotic trajectory properties of the proposed approach.     

Statistical Tests for Integrity Attacks on Cyber‐Physical Systems

This paper is written to show a new detection method for integrity attack on the measurements. It is proved that the normality of the residual error from Kalman filter is equivalent to that of the measurement. Our method utilizes this property and it could not only detect the distribution changes at current time, but also detect the its time correlations. Overall, it is an improved detection method for a wider range of attacks.     

图规划框架下的启发式搜索的研究与发展

随着智能规划研究的深入,以往的规划器已不能满足实际应用的需要.为了提高规划器求解实际问题的能力,启发式搜索产生了.对近10年来各种启发式搜索方法进行了分析,指出了它们的优缺点,并进行了比较.同时对智能规划及其启发式搜索的未来发展方向进行了分析与预测,旨在让研究和关心该领域的学者较为全面地了解这一领域.     

Planning with Multistep Forward Search with Forced Goal‐Ordering Constraints

To solve a real‐world planning problem with interfering subgoals, it is essential to perform early detection of subgoal dependencies and achieve the subgoals in the correct order. This is also the case for planning problems with forced goal‐ordering (FGO) constraints. In automated planning, forward search with FGO constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. Many existing methods such as goal agenda manager and ordered landmarks cannot detect the FGOs accurately, and thus, the undiscovered ordering relationship may cause the forward search to suffer from deadlocks. In this article, we put forward an approach via an effective search heuristic to constrain a planner to satisfy the FGOs. We make use of an atomic goal‐achievement graph in a look‐ahead search under the FGO constraints. This allows a forward search strategy to plan forward efficiently in multiple steps toward a goal state along a search path. Experimental results illustrate that, by avoiding deadlocks, we can solve more benchmark planning problems more efficiently than previous approaches. We also prove several formal properties for search that are related to FGO detection.     

基于启发式搜索的灵活规划的算法研究与系统实现

随着智能规划研究的深入,经典规划已不能满足实际应用的需要.本文分析了经典规划无法满足实际应用要求及产生灵活规划的原因.在对启发式搜索和灵活规划深入研究的基础上,提出了利用启发式搜索的方法来处理灵活规划问题的思想,并给出了基于启发式搜索的灵活规划算法和求解模型.采用智能规划中的基准问题对该算法进行测试,实验表明该方法在处理很多领域问题上都可以得到非常好的效果.     

结合增量与启发式搜索的多目标问题处理方法

提出了一种结合增量与启发式搜索的多目标问题处理方法,设计并实现了一个基于路径扩展方法的多目标增量启发式搜索系统.当问题搜索图中边的权重发生改变或添加删除节点时,该系统通过对搜索现场进行实时的更新,部分利用先前搜索保留的信息,从更新后的状态开始求解新的问题,从而提高了重搜索的效率.对gridworld标准测试样例进行了大量的系统测试,实验结果表明：结合增量与启发式搜索的处理方法能够有效地解决状态格局不断变化的一系列相似的多目标最短路径问题.     

Stochastic stability and robust stabilization of semi‐Markov jump linear systems

The semi‐Markov jump linear system (S‐MJLS) is more general than the Markov jump linear system (MJLS) in modeling some practical systems. Unlike the constant transition rates in the MJLS, the transition rates of the S‐MJLS are time varying. This paper focuses on the robust stochastic stability condition and the robust control design problem for the S‐MJLS with norm‐bounded uncertainties. The infinitesimal generator for the constructed Lyapunov function is first derived. Numerically solvable sufficient conditions for the stochastic stability of S‐MJLSs are then established in terms of linear matrix inequalities. To reduce the conservativeness of the stability conditions, we propose to incorporate the upper and lower bounds of the transition rate and meanwhile apply a new partition scheme. The robust state feedback controller is accordingly developed. Simulation studies and comparisons demonstrate the effectiveness and advantages of the proposed methods. Copyright © 2012 John Wiley & Sons, Ltd.     

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.      

Engineering an efficient two‐phase local search algorithm for the co‐rotating twin‐screw extruder configuration problem

The twin‐screw configuration problem arises during polymer extrusion and compounding. It consists in defining the location of a set of pre‐defined screw elements along the screw axis in order to optimize different, typically conflicting objectives. In this paper, we present a simple yet effective stochastic local search (SLS) algorithm for this problem. Our algorithm is based on efficient single‐objective iterative improvement algorithms, which have been developed by studying different neighborhood structures, neighborhood search strategies, and neighborhood restrictions. These algorithms are embedded into a variation of the two‐phase local search framework to tackle various bi‐objective versions of this problem. An experimental comparison with a previously proposed multi‐objective evolutionary algorithm shows that a main advantage of our SLS algorithm is that it converges faster to a high‐quality approximation to the Pareto front.     

Tracking Control for Stochastic Multi‐Agent Systems Based on Hybrid Event‐Triggered Mechanism

This paper deals with the leader‐following consensus for nonlinear stochastic multi‐agent systems. To save communication resources, a new centralized/distributed hybrid event‐triggered mechanism (HETM) is proposed for nonlinear multi‐agent systems. HETMs can be regarded as a synthesis of continuous event‐triggered mechanism and time‐driven mechanism, which can effectively avoid Zeno behavior. To model the multi‐agent systems under centralized HETM, the switched system method is applied. By utilizing the property of communication topology, low‐dimensional consensus conditions are obtained. For the distributed hybrid event‐triggered mechanism, due to the asynchronous event‐triggered instants, the time‐varying system method is applied. Meanwhile, the effect of network‐induced time‐delay on the consensus is also considered. To further reduce the computational resources by constantly testing whether the broadcast condition has been violated, self‐triggered implementations of the proposed event‐triggered communication protocols are also derived. A numerical example is given to show the effectiveness of the proposed method.     

Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

This article investigates the stochastic robust finite‐time boundedness problem for semi‐Markov jump uncertain (SMJU) neutral‐type neural networks with distributed and additive time‐varying delays (TDs). To derive less conservative stability criteria, a generalized reciprocally convex combination inequality (RCCI) is first proposed, which includes the existing RCCIs as its special cases. By taking full advantage of the characteristics of various TDs and SMJU parameters, a novel suitable Lyapunov‐Krasovskii functional is provided. Then, with the virtue of the new RCCI and other analysis approaches, some new criteria guaranteeing the underlying systems are stochastically robustly finite‐time bounded or stable and are derived in the form of linear matrix inequalities. Finally, three numerical examples are given to show the validity of the approaches presented in this article.     

Path Planning for Robots by Stochastic Optimization Methods

The problem of adaptive trajectory planning for robots under stochastic uncertainty is considered, where new information about the robots and their environment is presented on-line. Solving the problem numerically by means of spline approximation and by applying the method of neural networks, the optimal control can be calculated in real-time. Some numerical results are presented.     

Stochastic approximation with nondecaying gain: Error bound and data‐driven gain‐tuning

Our problem of interest is to minimize a sequence of scalar‐valued loss functions f_k(·) or to locate the root(s) of a sequence of vector‐valued functions g _k(·) corresponding to time with respect to a parameter . The available information for the minimization/root‐finding is the noise‐corrupted observation(s) of either f_k(·) or g _k(·) evaluated at a few of certain design points only. Taking both the dynamics and randomness into consideration, we use stochastic approximation (SA) algorithms to solve the time‐varying problem, but with a nondecaying gain (stepsize). The tracking performance of the nondecaying gain SA algorithm is guaranteed by a computable bound for the root‐mean‐squared error and mean‐absolute‐deviation. The only assumption imposed on the drifts is that the average distance between two consecutive underlying true parameters is bounded from above—this assumption allows the varying target to change abruptly. The error term arising from using the noisy evaluation of f_k(·) or g _k(·) in constructing the search direction is assumed to have bounded second‐moment—this allows for biased estimator to be used in the SA algorithms. Given the lenient assumptions on the drift and error term, the error bounds apply to a broad class of time‐varying scenarios and are useful for finite‐sample analysis. Since the tracking capability characterizes the average performance across all possible sample paths, it may not provide much guidance on the gain‐tuning strategies useful for one single run. Hence, we propose a data‐dependent gain‐tuning strategy based on estimating the Hessian information and the noise level. The adaptive rule is advantageous as it enables the step size to vary with information gathered during the progress of the estimation procedure.