Bounded space algorithms for variant of variable-sized bin packing

Given a list of items and a sequence of variable-sized bins arriving one by one, it is NP-hard to pack the items into the bin list with a goal to minimize the total size of bins from the earliest one to the last used. In this paper a set of approximation algorithms is presented for cases in which the ability to preview at most k（〉=2） arriving bins is given. With the essential assumption that all bin sizes are not less than the largest item size, analytical results show the asymptotic worst case ratios of all k-bounded space and offiine algorithms are 2. Based on experiments by applying algorithms to instances in which item sizes and bin sizes are drawn independently from the continuous uniform distribution respectively in the interval [0,u] and [u,l ], averagecase experimental results show that, with fixed k, algorithms with the Best Fit packing（closing） rule are statistically better than those with the First Fit packing（closing） rule.

Space-borne antenna adaptive anti-jamming method based on gradient-genetic algorithm

A novel space-borne antenna adaptive anti-jamming method based on the genetic algorithm （GA）, which is combined with gradient-like reproduction operators is presented, to search for the best weight for pattern synthesis in radio frequency （RF）. Combined, the GA＇s the capability of the whole searching is, but not limited by selection of the initial parameter, with the gradient algorithm＇s advantage of fast searching. The proposed method requires a smaller sized initial population and lower computational complexity. Therefore, it is flexible to implement this method in the real-time systems. By using the proposed algorithm, the designer can efficiently control both main-lobe shaping and side-lobe level. Simulation results based on the spot survey data show that the algorithm proposed is efficient and feasible.

Multi-objective Collaborative Optimization for Scheduling Aircraft Landing on Closely Spaced Parallel Runways Based on Genetic Algorithms

A scheduling model of closely spaced parallel runways for arrival aircraft was proposed,with multi-objections of the minimum flight delay cost,the maximum airport capacity,the minimum workload of air traffic controller and the maximum fairness of airlines′scheduling.The time interval between two runways and changes of aircraft landing order were taken as the constraints.Genetic algorithm was used to solve the model,and the model constrained unit delay cost of the aircraft with multiple flight tasks to reduce its delay influence range.Each objective function value or the fitness of particle unsatisfied the constrain condition would be punished.Finally,one domestic airport hub was introduced to verify the algorithm and the model.The results showed that the genetic algorithm presented strong convergence and timeliness for solving constraint multi-objective aircraft landing problem on closely spaced parallel runways,and the optimization results were better than that of actual scheduling.

Using Genetic Algorithms to Improve the Search of the Weight Space in Cascade－Correlation Neural Network

In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.

A NON－INCREMENTAL TIME－SPACE ALGORITHM FOR NUMERICAL SIMULATION OF FORMING PROCESS

A non-incremental time-space algorithm is proposed for numerical. analysis of forming process with the inclusion of geometrical, material, contact-frictional nonlinearities. Unlike the widely used Newton-Raphson counterpart, the present scheme features an iterative solution procedure on entire time and space domain. Validity and feasibility of foe present scheme are further justiced by the numerical investigation herewith presented.

A Fast Algorithm for Phase Grating Preparation by Real Space Method

Making use of this expression to calculate the phase grating in high resolution image simulation can greatly reduce the calculating time. In this paper, the derivation of the expression is introduced, and then the computer routine is explained in details. Finally the potential projection map of Mg44Rh7 along [001] direction is shown as an illustration. All operations are carried out in real space, so we call the calculation method as the real space method.

STATE SPACE TREE METHOD AND EXACT DECOMPOSITION ALGORITHM FOR FINDING NETWORK OVERALL RELIABILITY

First,the state space tree method for finding communication network overall re-liability is presented.It directly generates one disjoint tree multilevel polynomial of a networkgraph.Its advantages are smaller computational effort(its computing time complexity is O(en_l),where e is the number of edges and n_l is the number of leaves)and shorter resulting expression.Second,based on it an exact decomposition algorithm for finding communication network overallreliability is presented by applying the hypergraph theory.If we use it to carry out the m-timedecomposition of a network graph,the communication network scale which can be analyzed by acomputer can be extended to m-fold.

Multi-objective Evolutionary Algorithm Based on Target Space Partitioning Method

Considering the defects of conventional optimization methods, a novel optimization algorithm is introduced in this paper. Target space partitioning method is used in this algorithm to solve multi-objective optimization problem, thus achieve the coherent solution which can meet the requirements of all target functions, and improve the population's overall evolution level. The algorithm which guarantees diversity preservation and fast convergence to the Pareto set is applied to structural optimization problems. The empirical analysis supports the algorithm and gives an example with program.

Directional Routing Algorithm for Deep Space Optical Network

With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.

Existence and algorithm of solutions for a system of generalized mixed implicit equilibrium problems in Banach spaces

A new system of generalized mixed implicit equilibrium problems is introduced and studied in Banach spaces. First, the notion of the Yosida proximal mapping for generalized mixed implicit equilibrium problems is introduced. By using the notion, a system of generalized equation problems is considered, and its equivalence with the system of generalized mixed implicit equilibrium problems is also proved. Next, by applying the system of generalized equation problems, we suggest and analyze an iterative algorithm to compute the approximate solutions of the system of generalized mixed implicit equilibrium problems. The strong convergence of the iterative sequences generated by the algorithm is proved under quite mild conditions. The results are new and unify and generalize some recent results in this field.

基于遗传算法的划分序乘积空间问题求解层选择

划分序乘积空间作为一种新的粒计算模型,可以从多个视角和多个层次对问题进行描述和求解.其解空间是由多个问题求解层组成的格结构,其中每个问题求解层由多个单层次视角构成.如何在划分序乘积空间中选择问题求解层是一个NP难问题.为此,提出一种两阶段自适应遗传算法TSAGA(two stage adaptive genetic algorithm)来寻找问题求解层.首先,采用实数编码对问题求解层进行编码,然后根据问题求解层的分类精度和粒度定义适应度函数.算法第1阶段基于经典遗传算法,预选出一些优秀问题求解层作为第2阶段初始种群的一部分,从而优化解空间.算法第2阶段,提出随当前种群进化迭代次数动态变化的自适应选择算子、自适应交叉算子以及自适应大变异算子,从而在优化的解空间中进一步选择问题求解层.实验结果证明了所提方法的有效性.

ALGORITHM OF SOLUTIONS FOR MIXEDNONLINEAR VARIATIONAL-LIKE INEQUALITIES IN REFLEXIVE BANACH SPACE

In this paper, the author studies a class of mixed nonlinear variational-like inequalities in reflexive Banach space. By applying a minimax inequality obtained by the author, some existence uniqueness theorems of solutions for the mixed nonlinear variational-like inequalities are proved. Next, by applying the auxiliary problem technique, rite author suggests an innovative iterative algorithm to compute the approximate solutions of the mixed nonlinear variational-like inequalities. Finally, the convergence criteria is also discussed.

Stable Perturbed Algorithms for a New Class of Generalized Nonlinear Implicit Quasi Variational Inclusions in Banach Spaces

In this work, a new class of variational inclusion involving T-accretive operators in Banach spaces is introduced and studied. New iterative algorithms for stability for their class of variational inclusions and its convergence results are established.

复合联运物流运输网络建模与路径优化

在全球经济一体化背景下,物流行业成为不可或缺的经济支柱之一,在经济中的作用不断显现,复合联运逐渐成为物流行业的重要运输方式。以空铁海复合联运作为研究对象,构建不同的搜索空间,确定边界条件并分析货运影响参数,包括时间、成本、距离。以遗传算法为原则,求解建立空铁海复合联运模型并进行路径优化。确定3组节点组,每组都包含普通货物、特殊货物、航线拥堵3种情况,分别优化成本、时间、距离,为复合联运业务提供路径选择依据。通过分析,在规定的搜索空间内分别选出了最优时间路线、最优成本路线、最优距离路线。期望可以在物流运输过程中实现降本增效、减少风险、提高行业竞争力的目的。

交通规划导向的科技赋能交通小区划分方法

文章以手机信令、路网大数据为基础,提出了针对交通规划的科技赋能交通小区划分方法,以路网数据划分聚类单元,利用手机信令数据准确识别聚类单元内居民出行时空分布特征,基于改进耦合空间聚类算法进行聚类,借助现状路网对聚类结果进行修正得到交通小区划分结果。宁波市实例研究表明,科技赋能交通小区划分方法能兼顾居民出行时空特征和城市路网匹配,并能较好保证交通小区的整体连续性,该方法适用性强,可为交通规划的科学制定提供借鉴。

一种基于流形的机械臂动作构型知识压缩表达方法

针对在机械臂分拣任务中,存在物体形状各异、大小不一、训练神经网络成本过高的问题,提出一种基于流形空间的机械臂快速分拣方法。通过自主设计的一款简易实验装置模拟代替机械臂进行实验。对高维数据进行压缩,结合三维快速凸包求解算法,对体积大小不同的同类物体的流形空间进行分割,以凸包形式将稳定性较高的点集包裹起来。实验结果表明,体积大小不同的同类物体的高稳定流形子空间是一致的。该方法可以通过对一种物体的流形子结构进行尺度放缩,得到不同大小的同类物体的高稳定分拣区域,用于生成高效、可靠的机械臂分拣任务中的6D位姿构型,以提高分拣作业的工作效率。

An Orthogonal Wavelet Transform Fractionally Spaced Blind Equalization Algorithm Based on the Optimization of Genetic Algorithm

An orthogonal wavelet transform fractionally spaced blind equalization algorithm based on the optimization of genetic algorithm(WTFSE-GA) is proposed in viewof the lowconvergence rate,large steady-state mean square error and local convergence of traditional constant modulus blind equalization algorithm(CMA).The proposed algorithm can reduce the signal autocorrelation through the orthogonal wavelet transform of input signal of fractionally spaced blind equalizer,and decrease the possibility of CMA local convergence by using the global random search characteristics of genetic algorithm to optimize the equalizer weight vector.The proposed algorithm has the faster convergence rate and smaller mean square error compared with FSE and WT-FSE.The efficiency of the proposed algorithm is proved by computer simulation of underwater acoustic channels.

面向高维非线性最优化问题的粒子折跃算法

针对高维非线性最优化问题,在APSO和GA等智能算法的基础上,加入相邻对比、移动互斥思想,构建了一种新的智能算法——粒子折跃算法(Particle Warp,PW)。该算法通过对比每个粒子(解)在不同维度上的相近值构建虚拟粒子,计算适应度,为应对“早熟”现象,结合粒子跳跃的随机性、其他粒子对该粒子的排斥性,建立粒子折跃更新公式;针对CEC2017测试函数不同维度下最优化问题,利用PW与APSO、GA进行求解,分析了新算法的优越性,并将其应用于空间圆拟合中。研究表明,10种测试函数高维问题的求解中新算法体现了较强的优越性,在30维求解中优秀率达到90%;在空间圆拟合问题中,PW求取的适应度精确度比APSO提高了6.54%,比GA提高了6.93%,最优解更接近于设计值,最大偏差仅0.0864,满足实际需求,为工程实际中高维非线性最优化问题求解提供了一种新方法。

基于K-means聚类和特征空间增强的噪声标签深度学习算法

深度学习中神经网络的性能依赖于高质量的样本,然而噪声标签会降低网络的分类准确率。为降低噪声标签对网络性能的影响,噪声标签学习算法被提出。该算法首先将训练样本集划分成干净样本集和噪声样本集,然后使用半监督学习算法对噪声样本集赋予伪标签。然而,错误的伪标签以及训练样本数量不足的问题仍然限制着噪声标签学习算法性能的提升。为解决上述问题,提出基于K-means聚类和特征空间增强的噪声标签深度学习算法。首先,该算法利用K-means聚类算法对干净样本集进行标签聚类,并根据噪声样本集与聚类中心的距离大小筛选出难以分类的噪声样本,以提高训练样本的质量;其次,使用mixup算法扩充干净样本集和噪声样本集,以增加训练样本的数量;最后,采用特征空间增强算法抑制mixup算法新生成的噪声样本,从而提高网络的分类准确率。并在CIFAR10、CIFAR100、MNIST和ANIMAL-10共4个数据集上试验验证了该算法的有效性。

基于改进MH算法的室内空间自动布局

为了生成文博展厅、城市阅读空间、商场等具备公共服务职能的空间布局,提出了一种面向室内空间的自动布局方法。该方法首先针对多种布局的表达需求,将布局信息记录在层次化图结构中;再按照图结构填充适当的布局对象作为初始状态;利用能量函数和移动策略,引入改进的MH(Metropolis-Hastings)算法进行优化,生成合适的布局方案。选取多种类型的应用场景进行测试,结果证明了该方法的有效性。根据公共空间设计原则,提出评价指标比较各类方法性能。该方法在布置效率和运算效率方面具有优势,可应用于多种类型的公共空间布局设计。