Multi-directional local search

This paper introduces multi-directional local search, a metaheuristic for multi-objective optimization. We first motivate the method and present an algorithmic framework for it. We then apply it to several known multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. Experimental results show that our method systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort. We conclude that the proposed algorithmic framework is a viable option when solving multi-objective optimization problems.     

城市交叉口车辆速度与交通信号协同优化控制

为了降低城市交通中的行车延误与燃油消耗,针对人类驾驶车辆与自动驾驶车辆混合交通环境,提出一种基于交通信息物理系统(TCPS)的车辆速度与交通信号协同优化控制方法.首先,综合考虑路口交通信号、人类驾驶车辆、自动驾驶车辆三者之间的相互影响,设计一种适用于自动驾驶车辆与人类驾驶车辆混合组队特性的过路口速度规划模型;其次,针对车辆速度规划单一应用时的局限性,即无法减少车辆路口通行延误且易出现无解情况,提出一种双目标协同优化模型,能够综合考虑车辆速度规划与路口交通信号控制,同时降低车辆燃油消耗与路口平均延误.由于双目标优化问题求解的复杂性,设计一种遗传算法-粒子群算法混合求解策略.基于SUMO的仿真实验验证了所提出方法的有效性.     

基于CDMA下行链路的功率最优化调度

为CDMA下行链路多用户系统建立在用户时延限制下使基站平均功率最小的数学模型,将该优化问题转化为非约束Markov决策过程,用动态规划的方法获得最优解,并证明2个用户的功率-时延曲面是一个凸曲面。仿真结果证明,在CDMA下行链路多用户系统中,增加时延能节省功率且平均时延与功率仍具有凸函数关系。     

VHDL延时语句的综合算法研究

为了使得综合系统能够自动综合经设计迭代后由反向标注得到的时序信息,同时提高综合结果与模拟结果的时序一致性,对作为时序信息载体的延时语句的综合方法进行了研究,将延时语句考虑为延时约束,并提出了相应调度模型DTC_DFG及其调度算法。采用启发式机制使得调度算法的解空间搜索过程具有跳出局部最优的能力,此算法可在多项式时间复杂度下得到全局最优调度解。实验结果表明,该制度算法不仅能够有效综合延时语句,使得综合结果能够与模拟结果达到较好的一致,而且提供了一种给出延时约束的方便手段,减少了综合过程中的人工干预,极大地提高了设计效率。     

Lyapunov functionals in complex /spl mu/ analysis

Conditions for robust stability of linear time-invariant systems subject to structured linear time-invariant uncertainties can be derived in the complex /spl mu/ framework, or, equivalently, in the framework of integral quadratic constraints. These conditions can be checked numerically with linear matrix inequality (LMI)-based convex optimization using the Kalman-Yakubovich-Popov lemma. We show how LMI tests also yield a convex parametrization of (a subset of) Lyapunov functionals that prove robust stability of such uncertain systems. We show that for uncertainties that are pure delays, the Lyapunov functionals reduce to the standard Lyapunov-Krasovksii functionals that are encountered in the stability analysis of delay systems. We demonstrate the practical utility of the Lyapunov functional parametrization by deriving bounds for a number of measures of robust performance (beyond the usual H/sub /spl infin// performance); these bounds can be efficiently computed using convex optimization over linear matrix inequalities.     

基于新型双目标模型的约束优化进化算法

利用双目标模型求解约束优化问题时,由于它们的最优解集并不相等,因此需要增加特殊机制确保求解双目标问题的算法收敛到原问题的最优解.为克服这一缺点,本文首先将约束优化问题转化为新的双目标优化模型,并证明了新模型的最优解集与原问题的最优解集相等.其次,以简单的差分进化为搜索算法,基于多目标Pareto支配关系的非支配排序为选择准则,提出了求解新模型的差分进化算法.最后,用10个标准测试函数的数值试验说明了新模型及求解算法的有效性.     

A bi-objective reverse logistics network analysis for post-sale service

Reverse logistics, induced by various forms of return, has received growing attention throughout this decade. Reverse logistics network design is a major strategic issue. This paper addresses the analysis of reverse logistic networks that deal with the returns requiring repair service. A problem involving a manufacturer outsourcing to a third-party logistics (3PLs) provider for its post-sale services is proposed. First, a bi-objective optimization model is developed. Two objectives, minimization of the overall costs and minimization of the total tardiness of cycle time, are addressed. The facility capacity option at each potential location is treated as a discrete parameter. The purpose is to find a set of non-dominated solutions to the facility capacity arrangement among the potential facility locations, as well as the associated transportation flows between customer areas and service facilities. Then, a solution approach is designed for solving this bi-objective optimization model. The solution approach consists of a combination of three algorithms: scatter search, the dual simplex method and the constraint method. Finally, computational analyses are performed on trial examples. Numerical results present the trade-off relationship between the two objectives. The numerical results also show that the optimization for the first objective function leads to a centralized network structure; the optimization for the second objective function results in a decentralized network structure.     

A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints

This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0–1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time.     

基于SOVA算法的Turbo译码器的设计与优化

给出了基于SOVA算法的Turbo译码器的硬件设计系统结构，通过对关键模块的硬件资源占有及译码时序的分析，提出了减少硬件资源、降低硬件功耗以及提高译码速度、减少译码时延的优化设计方案。采用NC Simulator的仿真分析以及Cyclone II系列FPGA芯片的硬件测试表明，该文提出的优化设计方案减少了约40%的硬件资源，且译码速度提高了约60%，达到了降低功耗和提高速度的双重功效。     

Optimal management of wind and solar energy resources

This paper presents a portfolio-based approach to the harvesting of renewable energy (RE) resources. Our examined problem setting considers the possibility of distributing the total available capacity across an array of heterogeneous RE generation technologies (wind and solar power production units) being dispersed over a large geographical area. We formulate the capacity allocation process as a bi-objective optimization problem, in which the decision maker seeks to increase the mean productivity of the entire array while having control on the variability of the aggregate energy supply. Using large-scale optimization techniques, we are able to calculate – to an arbitrary degree of accuracy – the complete set of Pareto-optimal configurations of power plants, which attain the maximum possible energy delivery for a given level of power supply risk. Experimental results from a reference geographical region show that wind and solar resources are largely complementary. We demonstrate how this feature could help energy policy makers to improve the overall reliability of future RE generation in a properly designed risk management framework.     

Spark Streaming框架下的气象自动站数据实时处理系统

针对现有气象自动站业务平台面临处理数据不及时、交互式响应慢、统计时效差等问题,提出了使用Spark Streaming技术和HBase解决该问题的方法,将实时计算框架和分布式数据库系统结合起来实现大规模流式数据处理。使用Flume收集自动站数据,Spark Streaming对数据进行流式处理并存储到HBase数据库中,并设计Spark框架下的自动站数据流式入库处理算法和要素极值的实时统计算法,在Cloudera平台下实现了一个高速可靠的实时采集、处理、统计的应用系统。通过对比分析和性能监测,验证了该系统具有低延迟和高吞吐量的优势,运行状况良好,负载均衡。实验结果表明,Spark Streaming用于气象自动站的实时业务处理,数据并行写入HBase、基于HBase的查询和各类要素统计均能达到毫秒级响应,完全能满足自动站数据的应用需求,有效地支撑天气预报业务。     

MODEL SET IDENTIFICATION IN FREQUENCY-DOMAIN AND ITS APPLICATION TO JOINT DESIGN WITH ROBUST CONTROL

In this paper, we propose a new model set identification method for robust control, which determines both nominal models and uncertainty bounds in frequency-domain using periodgrams obtained from experimental data. This method also gives less conservative model sets when we have more experimental data, which is one of the distinguished features compared with the existing model set identification methods. To this end, first, we construct a new noise model set in terms of periodgrams, which consists of hard-bounded (or deterministic) noises but takes account of a low correlation property of noise signals, simultaneously. Then, based on the noise model, we show how to compute the nominal models and the upper bounds of modeling error via convex optimization, which minimize given cost functions. Furthermore, by introducing a weighting function compatible with control performance criterion into the identification cost function, we consider a joint design method of the proposed model set identification and H_∞ control. Numerical examples show the effectiveness of the proposed method.     

精确求解进港飞机调度双目标优化问题的epsilon约束算法

随着机场客流的持续增长,航班延误日益严重。同时,对于机场最重要的跑道资源而言,积雪结冰等会造成 飞机 打滑,从而出现事故。对于机场管理者,周期性地维护跑道至关重要,以防雨雪天气出现飞机打滑事故。该研究主要针对跑道上的 航班调度问题,考虑恶劣天气环境下跑道的周期性维护(如周期性喷洒除雪盐等)。为了在保证航班的服务质量的同时提高机场跑道的使用效率,文中以最小化航班总延误和跑道使用时间为优化的双目标。首先,提出该双目标优化问题混合整数规划模型;其次,为了精确求解出Pareto前沿,开发出epsilon约束算法;最后,给出算例来说明模型和算法的可行性。通过数学规划理论建模并开发精确求解算法,为机场资源优化研究提供参考。     

Robust performance optimization based on stochastic model errors: the stable case

Robust control aims to account for model uncertainty in design. Traditional methods for robust control typically assume knowledge of hard bounds on the system frequency response. However, this does not match well with system identification procedures which typically yield statistical confidence bounds on the estimated model. This paper explores a new procedure for obtaining a better match between robust control and system identification by using stochastic confidence bounds for robust control design. Given a nominal design, we set up an optimization problem which is aimed at reducing the statistical variability, measured in a mean square sense, from the nominal sensitivity. The proposed procedure is straightforward and leads to an easily computable solution for the final robust controller in the case of a stable plant and modest plant uncertainty. An illustrative example is provided which shows the advantages of the method. Copyright © 2002 John Wiley & Sons, Ltd.     

基于伪谱法的双摆吊车时间最优消摆轨迹规划策略

在工业生产过程中,桥式吊车系统经常会体现出双摆系统的特性,导致更多欠驱动状态量的出现,增大控制难度.基于此,论文提出了一种针对双摆桥式吊车系统的时间最优轨迹规划方法,可以得到全局时间最优且具有消摆能力的轨迹.具体而言,为方便地构造以时间为代价函数的优化问题,首先对系统运动学模型进行相应的变换;在此基础上,考虑包括两级摆角及台车速度和加速度上限值在内的多种约束,构造出相应的优化问题;然后,利用高斯伪谱法(Gauss-pseudospectral method, GPM)将该带约束的优化问题转化为更易于求解的非线性规划问题,且在转化过程中,可以非常方便地考虑轨迹约束.求解该非线性规划问题,即可得到时间最优的台车轨迹.不同于已有的大多数方法,该方法可获得全局时间最优的结果.最后,通过仿真与实验结果验证了这种时间最优轨迹规划方法具有满意的控制性能.     

CNB: A critical-network-based timing optimization method for standard cell global routing

A novel method, named critical-network-based (CNB),for timing optimization in global routing is presented in this paper.The essence of this method is different from that of the typical existing ones,named nets-based (NB) and critical-path-based (CPB).The main contribution of this paper is that the CNB delay reduction method is more efficient than the typical existing ones.This new method makes it possible to reduce the delay in an overall survey.Based on CNB,a timing optimization algorithm for global routing is implemented and tested on Microelectronics Center of North Carolina (MCNC) benchmarks in this paper.The experimental results axe compared between this algorithm and the existing ones.The experimental results show that this algorithm is able to control the delay efficiently.     

求解约束优化问题的佳点集多目标进化算法

结合数论中的佳点集理论和多目标优化方法,提出一种求解约束优化问题的进化算法。将约束优化问题转化为多目标优化问题,引入佳点集理论,以确保所构造的个体在搜索空间内分布均匀,设计变异算子增加个体多样性,采用分群局部搜索方式,并根据Pareto非支配关系选择群体中的优势个体。实验结果表明,该算法具有较好的稳定性。     

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also automatically resolves the issue of constraint scaling which is critical in real world and engineering optimization problems. The proposed unified approach converts the single-objective constrained optimization problem into a multi-objective problem. Evolutionary multi-objective optimization is used to solve the modified bi-objective problem and to estimate the penalty parameter automatically. The constrained optimum is further improved using classical optimization. The efficiency of the proposed method is validated on a set of well-studied constrained test problems and compared against without using normalization technique to show the necessity of normalization. The results establish the importance of scaling , especially in constrained optimization and call for further investigation into its use in constrained optimization research.     

考虑时延偏差的数字电路时延测试综述

先进集成电路工艺下,时延测试是数字电路测试的一项重要内容。各种时延偏差来源如小时延缺陷、工艺偏差、 串扰、电源噪声、老化效应等,影响着电路的额定时钟频率,是时延测试中需要考虑的因素。文章在介绍电路时延偏差 问题的各种来源的基础上,给出了针对不同的时延偏差问题所涉及的分析、建模、测试生成与电路设计等关键技术。进 一步介绍了中国科学院计算技术研究所近年来在考虑时延偏差的数字电路时延测试方面所做的研究工作,包括：考虑串 扰/电源噪声的时延测试、基于统计定时分析的测试通路选择、片上时延测量、超速测试、测试优化、在线时序检测等方 面。文章最后对数字电路时延测试技术的发展趋势进行了总结。     

Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach

This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.