改进HS共轭梯度算法及其全局收敛性

１．引 言 １９５２年 Ｍ．Ｈｅｓｔｅｎｅｓ和Ｅ．Ｓｔｉｅｆｅｌ提出了求解正定线性方程组的共轭梯度法［１］．１９６４年Ｒ．Ｆｌｅｔｃｈｅｒ和Ｃ．Ｒｅｅｖｅｓ将该方法推广到求解下列无约束优化问题： ｍｉｎｆ（ｘ）,ｘ∈Ｒｎ,（１）其中ｆ：Ｒｎ→Ｒ１为连续可微函数,记ｇｋ＝ ｆ（ｘｋ）,ｘｋ∈ Ｒｎ． 若点列｛ｘｋ｝由如下算法产生：其中 βｋ＝［ｇＴｋ（ｇｋ－ｇｋ－１）］／［ｄＴｋ－１（ｇｋ－ｇｋ－１）］．（Ｈｅｓｔｅｎｅｓ－Ｓｔｉｅｆｅｌ）　　（４）则称该算法为 Ｈｅｓｔｅｎｅｓ—Ｓｔｉｅｆｅｌ共轭梯度算…     

U1－sr条件

本文讨论Ｕ１－ｓｒ条件，这一条件有益于计算环的Ｋ１群．得到主要结果为；（１）完全确定满足Ｕ１－ｓｒ条件的半局部环：（２）给出使ＥｎｄＲ（Ｍ）满足Ｕ１－ｓｒ条件的一个刻划；（３）引进比Ｕ１－ｓｒ更强的一个条件ＳＵ１－ｓｒ，利用上述结果证明了：若Ｒ∈ＳＵ１－ｓｒ，则Ｍｎ（Ｒ）∈Ｕ１－ｓｒ；（４）证明了对于满足ＳＵ１－ｓｒ的环Ｒ，Ｋ１Ｒ＝ＧＬ１（Ｒ）ａｂ．     

一种级数的维数

令其中Ｓ∈（１，２），对ｎ∈ｚ，ｉ）若ｂｎ＋１／ｂｎ单凋趋于无穷．则图形Ｇ（ｆ，Ｉ）的Ｈａｕｓｄｏｒｆｆ维数ｉｉ）若ｂｎ＝ｂｎ（ｂ＞１），则Ｓ≥ｄｉｍＧ（ｆ，Ｉ）≥Ｓ－Ｃｂ１－ｓ其中Ｃ是一个常数．     

S~1 × S~1-Borsuk-Ulam Theorem on Product Spaces

Ｓ~１×Ｓ~１－Ｂｏｒｓｕｋ－ＵｌａｍＴｈｅｏｒｅｍｏｎＰｒｏｄｕｃｔＳｐａｃｅｓＺｈｏｎｇＣｈｅｎｇｋｕｉ（钟承奎）（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，ＬａｎｚｈｏｕＵｎｉｖｅｒｓｉｔｙ，Ｌａｎｚｈｏｕ，７３００００）Ａｂｓｔｒａｃｔ：...     

Integral Solution Operators for the Cauchy－Riemann Equations on an Open Set with Piecewise C~1－Boundary

ＩｎｔｅｇｒａｌＳｏｌｕｔｉｏｎＯｐｅｒａｔｏｒｓｆｏｒｔｈｅＣａｕｃｈｙ－ＲｉｅｍａｎｎＥｑｕａｔｉｏｎｓｏｎａｎＯｐｅｎＳｅｔｗｉｔｈＰｉｅｃｅｗｉｓｅＣ￣１－ＢｏｕｎｄａｒｙＭａＺｈｏｎｇｔａｉ（马忠泰）（Ｄｅｐｔ．ｏｆＭａｔｈ．Ｓｈａｎｄｏｎ...     

A Restriction Theorem on the Reduced Product Heisenberg Group

§１．　ＩｎｔｒｏｄｕｃｔｉｏｎＬｅｔΩｂｅａｒｅｇｕｌａｒｃｏｎｅｉｎＲｎ,Φ：Ｃｍ×Ｃｍ→Ｃｎ＝Ｒｎ＋ｉＲｎａｎΩ－ｐｏｓｉｔｉｖｅＨｅｒｍｉｔｉａｎｍａｐ．ＴｈｅＳｉｅｇｅｌｄｏｍａｉｎＤΩ,ΦｏｆｔｙｐｅｔｗｏｉｎＣｎ×ＣｍｉｓｄｅｆｉｎｅｄｂｙＤΩ,Φ＝｛（ｚ,ｗ）∈：Ｃｎ×Ｃｍ：Ｉｍｚ－Φ（ｗ,ｗ）∈Ω｝（１）（ｓｅｅ［６］）．Ｓｐｅｃｉａｌｌｙ,ｗｅａｓｓｕｍｅｔｈａｔｎ＝ｍ,Ω＝｛ｔ＝（ｔ１,ｔ２,…,ｔｎ）∈Ｒｎ：ｔｉ＞０,ｉ＝１,２,…,ｎ｝,Φ（ｕ,ｖ）＝ｕ·ｖ＝（ｕ１ｕ…     

GLOBAL ATTRACTOR OF A SPATIALLY DISCRETIZED REACTION-DIFFUSION SYSTEM WITH HAMILTONIAN STRUCTURE

１ＨａｍｉｌｔｏｎｉａｎＳｔｒｕｃｔｕｒｅｉｎＲｅａｃｔｉｏｎ－ＤｉｆｕｓｉｏｎｓｙｓｔｅｍＣｏｎｓｉｄｅｒａｓｙｓｔｅｍｏｆｒｅａｃｔｉｏｎ－ｄｉｆｕｓｉｏｎｅｑｕａｔｉｏｎｓｕｔ＝ｕｘｘ＋ｆ（ｕ，ｖ）ｖｔ＝ｖｘｘ＋ｇ（ｕ，ｖ）｛（１．１）ｗｈｅｒ...     

非线性波动方程解的Blow up

本文主要讨论高维空间非线性波动方程的Ｃａｕｃｈｙ问题整体解的非存在性，我们证明对Ｕｕ－△ｕ＝ｆ（ｕ），ｆ（ｕ）＝ｃ｜ｕ｜ｐ－１ｕ，当１＜ｐ≥时，若初始能量非正，则无论初值数据的ｃｋ－范数（连续空间范数）多么小，解按Ｃｋ－范数或按Ｈｓ（Ｒｎ）（Ｓ≥１）都在有限时间内Ｂｌｏｗｕｐ，并且有相同的生命跨度．     

Zero-free Regions for Dirichlet L-functions and the Exceptional Set of Goldbach Numbers

Ｚｅｒｏ－ｆｒｅｅＲｅｇｉｏｎｓｆｏｒＤｉｒｉｃｈｌｅｔＬ－ｆｕｎｃｔｉｏｎｓａｎｄｔｈｅＥｘｃｅｐｔｉｏｎａｌＳｅｔｏｆＧｏｌｄｂａｃｈＮｕｍｂｅｒｓＬｉＨｏｎｇｚｅ（李红泽）ＭＲ（１９９１）ＳｕｂｊｅｃｔＣｌａｓｉｆｉｃａｔｉｏｎ：１１Ｎ１３，１...     

ABOUT NON-COOPERATIVE N-PERSON GAMES

ＡＢＯＵＴＮＯＮ－ＣＯＯＰＥＲＡＴＩＶＥＮ－ＰＥＲＳＯＮＧＡＭＥＳ￥ＺｈａｎｇｙａｄｏｎｇａｎｄＺｈａｎｇｓｈｅｎｇｋａｉ（Ｎｏ１ＢａｏｄｉｎｇＳｔ，ＩｎｓｔｉｔｕｔｅｏｆＯ－Ｒ．１１６００１．ＤａｌｉａｎＣｈＩＮＡ）Ａｂｓｔｒａｃｔ：Ｉｎｔｈｅｐａ...     

蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

开放式多中心需求可拆分VRP及混沌遗传模拟退火算法

综合考虑客户需求量允许被拆分、配送系统拥有多个配送中心且车辆可就近返回配送中心的运输模式,建立以配送中心日均建设成本、车辆派遣成本、理货成本和油耗成本之和最小为目标的数学模型,并根据问题特征设计了混沌遗传模拟退火算法对问题进行求解。通过对不同规模以及不同类型的算例进行实验,验证了混沌遗传模拟退火算法求解本文问题的有效性。     

遗传算法的物理解释

提出了一种理想化的模拟仿生搜索算法——扰动算法 ,以此方法为基础 ,分析了遗传算法的搜索过程和效率问题 ,阐明了遗传算法作为一种次优算法的有效性 .相对于遗传算法的生物解释 ,本文给出了相应的物理解释 .同时 ,本文为遗传算法、进化策略和模拟退火算法找到了一种统一的物理解释 ,揭示了这些重要的仿生类算法实质上的相似性 .     

A two-stage intelligent search algorithm for the two-dimensional strip packing problem

This paper presents a two-stage intelligent search algorithm for a two-dimensional strip packing problem without guillotine constraint. In the first stage, a heuristic algorithm is proposed, which is based on a simple scoring rule that selects one rectangle from all rectangles to be packed, for a given space. In the second stage, a local search and a simulated annealing algorithm are combined to improve solutions of the problem. In particular, a multi-start strategy is designed to enhance the search capability of the simulated annealing algorithm. Extensive computational experiments on a wide range of benchmark problems from zero-waste to non-zero-waste instances are implemented. Computational results obtained in less than 60 seconds of computation time show that the proposed algorithm outperforms the supposedly excellent algorithms reported recently, on average. It performs particularly better for large instances.     

B2C电子商务仓库拣货路径优化策略应用研究

当前国内B2C电子商务仓库多为人至物的拣货模式,拣货作业成为其核心作业之一,占据仓库大量时间成本和资金成本,拣货路径优化成为企业亟需解决的问题。本文基于TSP对拣货路径进行建模,利用蚁群算法、模拟退火算法和禁忌搜索对该NP-hard问题进行求解,并同当前企业普遍采用的S型启发式策略进行对比,拣货时间节约13.35%。进一步得出当拣货品数量较少时应采用模拟退火算法求解,而当拣货品数量较大时采用蚁群算法仅进行一次迭代,则可以实现短时间得到相对较优的解。所得结果已应用于某大型电子商务企业,效果明显。     

混合模拟退火-进化策略在非线性参数估计中的应用

提出了一种混合模拟退火-进化策略算法应用在非线性参数估计中,方法克服了传统优化方法估计参数精度不高且容易陷入局部极小值等缺点,并且将模拟退火算法和进化策略算法相结合,充分发挥各自算法优点.最后通过给出非线性参数估计算例,结果表明,算法具有参数估计精度较高,收敛速度快,自适应性强,在实际工程中有较大的应用价值.     

AN ADAPTIVE MEMBRANE ALGORITHM FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

Membrane algorithms （MAs）, which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.     

An adaptive membrane algorithm for solving combinatorial optimization problems

Membrane algorithms (MAs), which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The empirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural network and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.     

变邻域模拟退火算法求解速度时变的VRPTW问题

本文在经典的带时间窗的车辆路径问题(VRPTW)的基础上,考虑不同时间段车辆行驶速度不同的情况,研究速度时变的带时间窗车辆路径问题(TDVRPTW),使问题更具实际意义。本文用分段函数表示不同时间段下的车辆行驶速度,并解决了速度时变条件下行驶时间计算的问题。针对模拟退火算法(SA)在求解VRPTW问题时易陷入局部最优解,变邻域搜索算法(VNS)在求解VRPTW问题时收敛速度慢的问题,本文将模拟退火算法以一定概率接受非最优解的思想和变邻域搜索算法系统地改变当前解的邻域结构以拓展搜索范围的思想结合起来,提出了一种改进的算法——变邻域模拟退火算法(SAVN),使算法在退火过程中一陷入局部最优解就改变邻域结构,更换搜索范围,以此提升算法跳出局部最优解的能力,加快收敛速度。通过在仿真实验中将SAVN算法的求解结果与VNS算法、SA算法进行对比,验证了SAVN算法确实能显著提升算法跳出局部最优解的能力。