Distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm

A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload.

Nested Genetic Algorithm for Resolving Overlapped Spectral Bands

A nested genetic algorithm, including genetic parameter level and genetic implemented level for peak parameters, was proposed and applied for resolving overlapped spectral bands. By the genetic parameter level, parameters of generic algorithm were optimized; moreover, the number of overlapped peaks was determined simultaneously Then parameters of individual peaks were computed with the genetic implemented level.

Use the Power of a Genetic Algorithm to Maximize and Minimize Cases to Solve Capacity Supplying Optimization and Travelling Salesman in Nested Problems

Using Genetic Algorithms (GAs) is a powerful tool to get solution to large scale design optimization problems. This paper used GA to solve complicated design optimization problems in two different applications. The aims are to implement the genetic algorithm to solve these two different (nested) problems, and to get the best or optimization solutions.

Algorithm for 2D irregular-shaped nesting problem based on the NFP algorithm and lowest-gravity-center principle

The nesting problem involves arranging pieces on a plate to maximize use of material. A new scheme for 2D ir- regular-shaped nesting problem is proposed. The new scheme is based on the NFP (No Fit Polygon) algorithm and a new placement principle for pieces. The novel placement principle is to place a piece to the position with lowest gravity center based on NFP. In addition, genetic algorithm (GA) is adopted to find an efficient nesting sequence. The proposed scheme can deal with pieces with arbitrary rotation and containing region with holes, and achieves competitive results in experiment on benchmark datasets.

Computationally Efficient Direction of Arrival Estimation for Improved Nested Linear Array

Nested linear array enables to enhance localization resolution and achieve under-determined direction of arrival(DOA)estimation.In this paper,the traditional two-level nested linear array is improved to achieve more degrees of freedom(DOFs)and better angle estimation performance.Furthermore,a computationally efficient DOA estimation algorithm is proposed.The discrete Fourier transform(DFT)method is utilized to obtain coarse DOA estimates,and subsequently,fine DOA estimates are achieved by spatial smoothing multiple signals classification(SS-MUSIC)algorithm.Compared to SS-MUSIC algorithm,the proposed algorithm has the same estimation accuracy with lower computational complexity because the coarse DOA estimates enable to shrink the range of angle spectral search.In addition,the estimation of the number of signals is not required in advance by DFT method.Extensive simulation results testify the effectiveness of the proposed algorithm.

Recognizing Expression Variant and Occluded Face Images Based on Nested HMM and Fuzzy Rule Based Approach

The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.

Genetic Algorithms to the Nesting Problem in the Leather Manufacturing Industry

The nesting problem in the leather manufacturing is the problem of placing a set of irregularly shaped pieces (called stencils) on a set of irregularly shaped surfaces (called leathers sheets). This paper presents a novel and promising processing approach. After the profile of leather sheets and stencils is obtained with digitizer, the discretization makes the processing independent of the specific geometrical information. The constraints of profile are regarded thoroughly. A heuristic bottom-left placement strategy is employed to sequentially locate stencils on sheets. The optimal placement sequence and rotation are deterimined by genetic algorithms (GA). A natural concise encoding method is developed to satisfy all the possible requirements of the leather nesting problem. The experimental results show that the proposed algorithm can not only be applied to the normal two-dimensional nesting problem, but also especially suitable for the placement of multiple two-dimensional irregular stencils on multiple two-dimensional irregular sheets.

Fresh views on some recent developments in the simplex algorithm

First, the main procedures and the distinctive features of the most-obtuse-angle（MOA）row or column pivot rules are introduced for achieving primal or dual feasibility in linear programming. Then, two special auxiliary problems are constructed to prove that each of the rules can be actually considered as a simplex approach for solving the corresponding auxiliary problem. In addition, the nested pricing rule is also reviewed and its geometric interpretation is offered based on the heuristic characterization of an optimal solution.

IAGNES algorithm for protocol recognition

In the process of protected protocol recognition,an improved AGglomerative NESting algorithm( IAGNES) with high adaptability is proposed,which is based on the AGglomerative NESting algorithm( AGNES),for the challenging issue of how to obtain single protocol data frames from multiprotocol data frames. It can improve accuracy and efficiency by similarity between bit-stream data frames and clusters,extract clusters in the process of clustering. Every cluster obtained contains similarity evaluation index which is helpful to evaluation. More importantly,IAGNES algorithm can automatically recognize the number of cluster. Experiments on the data set published by Lincoln Laboratory shows that the algorithm can cluster the protocol data frames with high accuracy.

考虑水土互馈关系的区域水土资源联合优化配置

针对水土资源空间不匹配、水资源总量不足及土地资源利用程度不高等问题,以二元水循环理论为基础,构建考虑水土互馈关系的区域水土资源联合优化配置模型。该模型包括产水模块、水土联合配置模块和土地模拟模块,以GDP最大和基于生态绿当量的区域植被覆盖率(EGE-RVC)最高为目标函数,并提出了一种耦合约束法、逐次逼近法和非线性规划的三层嵌套算法进行求解。以河南省洛阳市为例对模型和方法进行验证,结果表明,洛阳市可利用水量增加了4463万m^(3),GDP提高了12.5%,EGE-RVC增加了2.6%,各用地类型缺水率不同程度下降。研究成果能够为区域水土资源优化配置研究提供新的思路和技术参考。

基于轨迹线改进的临界多边形算法

在异形件的下料排样问题中,最为困难的就是求解裁片在板料中的位置以保证材料较高的利用率,算法复杂度随着料片数量和料片轮廓复杂度的增加迅速上升。临界多边形算法是计算异形件之间靠接位置和重叠关系的一种基础性几何工具,临界多边形算法的性能与下料排样算法效率密切相关。本文在基于轨迹线的求解临界多边形算法基础上,提出一种求解临界多边形(NFP)的改进算法。该算法有效地将移动碰撞算法和轨迹线算法相结合,充分发挥两类算法各自的优点,提高了临界多边形求解的计算速度。仿真实例验证了改进方法的正确性和有效性。

基于最优觅食算法的增材制造中多种类零件分批排样研究

增材制造中多种类零件分批排样存在打印时间成本高和工作台空间利用率低等问题,且需考虑零件高度的影响。分批排样问题包括零件在工作台上放置策略和成型批次分配两个子问题。放置策略涉及成型方向选择、零件碰撞检测和定位策略。基于建立的成型方向准则,利用多边形表示零件投影轮廓,提出基于临界多边形的改进移动碰撞法以确定3种不同形态多边形零件的免碰撞排放范围,提出新的左下定位策略放置零件,新定位策略融合了改进的建设性方法和契合度;提出单机台面积占用最大化策略实现零件成型批次分配。基于上述研究,提出基于最优觅食算法的分批排样算法,算法采用双重编码表达零件放置顺序和旋转角度,以最小化完工时间为目标实现优化分批排样。以案例库零件为对象,与3种对比算法比较,表明所提方法的方案能有效提高增材制造的空间利用率和缩短完工时间。

众包制造环境下协同产品族设计与延迟决策的主从关联优化

针对众包制造环境下产品族设计与延迟决策之间的协同优化问题,建立了以产品族设计为主、延迟决策为从的混合整数非线性双层规划模型。模型上层由制造商设计产品族架构以最大化自身的期望利润,下层由多个分销商优化延迟产品模块的类型以最大化各自的期望利润,且各分销商之间的决策具有相互独立性。考虑到延迟产品模块事先不确定,提出虚拟延迟结构将该优化问题具体化,并证明需要被延迟的产品模块。开发了一个双层嵌套遗传算法对模型进行求解。用智能冰箱产品族延迟案例验证了所提模型和算法的有效性,并对所提延迟偏好参数进行了灵敏度分析。

考虑柔性资源多维价值标签的交直流配电网灵活调度

可再生能源高渗透率使得交直流配电网面临严重的随机性和波动性,充分挖掘柔性资源调控潜力可以有效地降低可再生能源带来的网络运行风险。针对当前交直流配电网优化调度主要考虑电压源型换流器(VSC)的调控能力,对VSC与柔性资源协同研究不足的问题,提出一种考虑柔性资源多维价值标签的交直流配电网灵活调度方法。首先,考虑柔性资源调控特性差异,构建了包含响应时段、调节能力、调节成本及响应意愿的多维价值标签评估体系,准确全面地评估柔性资源聚合调控潜力范围;其次,以运行成本最小为目标函数,柔性资源多维价值标签为约束,建立交直流配电网两阶段鲁棒日前优化调度模型,并采用嵌套列和约束生成算法(C&CG)及强对偶理论进行求解;最后,通过改进的IEEE 33节点交直流配电网仿真结果表明,所提方法能够充分发挥柔性资源的调控潜力和协同能力,同时可有效提高模型求解效率和求解精度。

基于NAGA的物流企业自有及外包运力配置鲁棒优化

为深入优化物流企业的自有及外包运力配置,综合考虑运输需求不确定性和运输车型异质性,以全年运输利润最大化为目标,构建了多车型参与下运力配置鲁棒优化模型.依据决策变量的层次特性,设计了一种内外双层结构的嵌套式自适应遗传算法(NAGA).依托上海某物流企业的实际数据,进行了需求确定及不确定情形下的实例分析.结果表明,所构建的鲁棒优化模型兼顾经济性与鲁棒性,NAGA能对该模型进行高效求解.在需求确定情形下,优化后的运输利润提高了6.2%,且NAGA在求解质量及稳定性方面表现优于典型嵌套式启发算法;在需求不确定情形下,采用所提出的运力配置方法,决策者可根据市场波动水平及风险偏好调整鲁棒优化模型参数取值,以灵活获取合适的自有及外包运力配置方案.

块调度模式下的智慧小区自动需求响应模型

智慧城市概念的兴起催生了一大批智慧小区试点项目,其完备的物联、调控和量测体系可支撑自动需求响应(automated demand response,ADR)的顺利实施,如何设计智慧小区ADR模式来提高智慧小区运营商的需求响应可靠性、鲁棒性和成本效益,显得十分有必要。为此,提出了基于鲁棒优化的ADR模型,该模型以智慧小区经济效益最大为目标,为确保ADR调度计划的高度可实施性,设计了用于解决时段连续型ADR模式与居民用户用电规律相悖的块ADR策略,以及以所辖燃气轮机来平抑风电预测偏差的策略。所提模型通过嵌套分割算法求解,可保障最优解收敛于全局可行域。算例是在修改后的Garver-6节点系统中进行的,验证了所提模型的有效性。

含混合式抽水蓄能电站的水光蓄日内互补运行研究

在传统水电机组基础上加入可逆式机组构成混合式抽水蓄能电站增加水电调节能力,可与光伏电站联合运行,平抑新能源发电的不稳定性。为研究不同季节典型日下,含混合式抽水蓄能电站的水光蓄互补运行方式,构建以发电量最大和出力波动最小为目标的水光蓄互补运行短期调度模型,提出双层嵌套模型求解思路,考虑光伏出力的波动性,求解不同运行工况下含混合式抽水蓄能电站的水光蓄互补运行短期调度模型。计算结果表明,在不同来水及不同光伏出力过程的前提下,混合式抽水蓄能电站可采取不同运行工况以满足目标函数,不仅可以促进光伏并网消纳,还可增加水电发电量,减少弃水,为大量光伏资源并网下混合式抽水蓄能电站运行方式提供参考。

产品族架构设计与供应链延迟决策的主从交互优化

鉴于延迟策略的研究较少关注到产品族架构设计与整条供应链延迟决策之间存在的内在交互影响,提出对产品族架构设计与供应链延迟决策的一种主从交互优化方法。通过构建在其之间的主从交互决策机制,建立了一个以产品族架构设计为主、供应链延迟决策为从的非线性双层规划模型。模型上层是开发商设计产品族架构和决策其中的延迟产品模块类型,从而最大化单位成本的顾客效用;下层的决策主体包括多个供应商、多个制造商、多个延迟承包商及多个分销商,它们分别通过优化产品族的延迟制造过程来最小化各自的运营成本。针对模型求解的复杂性,设计了一种嵌套遗传算法进行求解。以智能冰箱产品族延迟生产案例验证所提优化模型和求解算法的可行性,并通过对多项式分对数选择规则中的参数θ进行灵敏度分析实验得出了一些管理启示。

An Adaptive Local Grid Nesting-based Genetic Algorithm for Multi-earth Observation Satellites' Area Target Observation

The Scheduling of the Multi-EOSs Area Target Observation(SMEATO)is an EOS resource schedul-ing problem highly coupled with computational geometry.The advances in EOS technology and the ex-pansion of wide-area remote sensing applications have increased the practical significance of SMEATO.In this paper,an adaptive local grid nesting-based genetic algorithm(ALGN-GA)is proposed for developing SMEATO solutions.First,a local grid nesting(LGN)strategy is designed to discretize the target area into parts,so as to avoid the explosive growth of calculations.A genetic algorithm(GA)framework is then used to share reserve information for the population during iterative evolution,which can generate high-quality solutions with low computational costs.On this basis,an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is sufficient.The effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different scales.The results show that the ALGN-GA offers advantages over several conventional algorithms in 88.9%of instances,especially in large-scale instances.These fully demonstrate the high efficiency and stability of the ALGN-GA.

基于嵌套蚁群算法的机器人拣货作业联合优化

针对物流仓储中心拣货作业过程中系统订单分批和拣货路径分步拣选效率低的问题,提出一种基于嵌套蚁群的订单分批和路径优化的联合拣货策略。首先,建立以最小化总路径为目标的订单分批与拣货路径联合优化模型;然后,考虑双重优化的复杂性,设计一种嵌套蚁群算法对模型进行求解,以订单分批模型为基准不断优化订单分批结果,得出最优分批集合单,其优化集合单再嵌套蚁群算法实现拣货路径优化。为验证该算法对随机订单有效性,抽取某一天17:00-18:00时段内既有货架区货物又有地堆区货物的43个订单算例进行仿真实验,与传统订单分批和拣货路径分步拣选策略相比,基于嵌套蚁群算法的拣货作业联合优化模型的随机订单拣货路径更短、拣货时间更少,经过联合优化后,机器人总拣选距离缩短了170 m。基于嵌套蚁群算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题,为配送中心拣选系统的优化提供依据。