基于不同信息素更新策略的卫星数传调度蚁群优化算法

针对具有时间窗口和数传资源限制卫星数传调度问题,提出了基于解构造图模型的蚁群优化算法.借鉴精英机制,设计了绝对精英策略、相对精英策略、收益精英策略和对等精英策略等四种信息素更新策略.通过对不同规模场景的仿真试验,验证了基于不同信息素更新策略的蚁群算法是求解卫星数传调度问题的有效途径.基于信息素平衡思想的相对精英策略、收益精英策略和对等精英策略相对于绝对精英策略而言,能够避免算法过早陷入局部最优或出现退化行为,在规模较大的场景中能够收敛到比绝对精英策略更优的解.在小规模场景中,相对精英策略和收益精英策略所得解最好,而在大规模场景中对等精英策略所得解最好.     

关于最大权k－子集分拆问题

对给定规模为ｎ的集合Ｓ，其每一个规模至多为ｋ的子集对应一个权．本文研究如何将Ｓ分为　个互不相交的规模至多为ｋ的子集且满足权和最大的问题．我们证明了该问题当ｋ＝２时是多项式时间可解的；当ｋ≥３时为ＮＰ－完全的；同时给出了一个Ｏ（ｎ￣（ｋ＋１））时间的启发式算法，所得到的解与最优解之比不小于１／ｋ．     

一个带限制的椭圆特征问题的多解和变号解

利用变分方法证明了一个带限制的半线性椭圆特征问题变号解的存在性.所获得的3个解,1个是正解、1个是负解、1个是变号解.在某些附加条件下证明了多重变号解的存在性.     

有资格限制的指派问题的求解方法

在实际的指派工作中，常会遇到某个人有没有资格去承担某项工作的问题，因此，本建立了有资格限制的指派问题的数学模型。在此数学模型中，将效益矩阵转化为判定矩阵，由此给出了判定此种指派问题是否有解的方法；在有解的情况下，进一步将效益矩阵转化为求解矩阵，从而将有资格限制的指派问题化为传统的指派问题来求解。最后给出了一个数值例子来说明这样的处理方法是有效的。     

一类多商品设施选址问题的基于线性松弛解的启发式方法

多商品设施选址问题是众多设施选址问题中一类重要而困难的问题.在这一问题中,顾客的需求可能包含不止一种商品.对于大规模问题,成熟的商业求解器往往不能在满意的时间内找到高质量的可行解.研究了无容量限制的单货源多商品设施选址问题的一般形式,并给出了应用于此类问题的两个启发式方法.这两个方法基于原选址问题的线性规划松弛问题的最优解,分别通过求解紧问题和邻域搜索的方式给出了原问题的一个可行上界.理论分析指出所提方法可以实施于任意可行问题的实例.数值结果表明所提方法可以显著地提高求解器求解此类设施选址问题的求解效率.     

海洋波导的可穿透障碍物散射场远场分布的若干性质

研究了海洋波导中可穿透目标时谐声波散射传播远场分布的性质,构造了透射问题解的集合,使得所构造解的集合在边界上的限制在某个Hilbert空间中是稠密的,确定了传播远场分布的集合在某个Hilbert空间中是完备的.这些性质对研究海洋波导中的逆透射问题有重要的应用.     

短距离竞技游泳运动的推进力最优化分析初探

通过建立竞技游泳运动的动力学模型和能量转换模型，并运用最优控制理论对于在给定时间内所游进的路程进行了最优化分析，结合奥运会记录和前人实验结果推导出在一定假设和限制条件下推进力的最优解，得到了与实际情况吻合的最佳速度和体能分配方案．     

解三角函数问题要注意隐含条件

在三角函数部分 ,利用三角函数的图像、性质及公式进行三角函数的求值、化简和证明是基本的内容 ,而求值必须分清是多值还是单值 ,化简和证明要做到严谨、言之有理 .因此 ,在解三角函数问题时 ,一定要注意角的限制条件 ,特别是那些不易被发现的隐含条件 .一、注意挖掘题设中的隐含条件 ,正确解题三角中的有些问题 ,已知中虽然没有明确角的具体范围 ,但题设中给出的数据对角的范围有限制 ;还有些问题即使给出了角的某一范围 ,但所给数据对角的范围作了进一步的限制 .解题中如若没有发现题设中的隐含条件 ,极易出现错解 .例 1　已知 3ｓｉｎ2 …     

用正切函数解题时应注意的问题

老师们常有这个体会:尽管在教正切函数这一节时,教师一再强调要注意正切函数定义域的限制,但是学生在解题时,却还是常常忽视这种限制,致使解答或不全面,或不严密,甚至产生错误的答案。例如学生在解三角函数有关问题的时候,由于忽视这种限制所产生的种种错误,归纳起来就有许多种。现列举几例以供今后解正切函数有关问题时参考。     

多重网格法和自适应算法的组合及其对非线性Schrdinger方程的应用

在解非线性的进化型偏微分方程时,为了数值计算的稳定性常常采用无条件稳定的隐式差分格式.这样会引起两个问题:一是要解线性甚至非线性的代数方程组,这是费时间的;另一是在解代数方程组时,迭代法的收敛性依赖于时间步长,特别是非线性迭代的收敛性会对时间步长加以严格的限制.     

Parallelizing simulated annealing algorithms based on high-performance computer

We implemented five conversions of simulated annealing (SA) algorithm from sequential-to-parallel forms on high-performance computers and applied them to a set of standard function optimization problems in order to test their performances. According to the experimental results, we eventually found that the traditional approach to parallelizing simulated annealing, namely, parallelizing moves in sequential SA, difficultly handled very difficult problem instances. Divide-and-conquer decomposition strategy used in a search space sometimes might find the global optimum function value, but it frequently resulted in great time cost if the random search space was considerably expanded. The most effective way we found in identifying the global optimum solution is to introduce genetic algorithm (GA) and build a highly hybrid GA+SA algorithm. In this approach, GA has been applied to each cooling temperature stage. Additionally, the performance analyses of the best algorithm among the five implemented algorithms have been done on the IBM Beowulf PCs Cluster and some comparisons have been made with some recent global optimization algorithms in terms of the number of functional evaluations needed to obtain a global minimum, success rate and solution quality.     

A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm. This work was supported by the University of Colorado at Colorado Springs.     

New Reflection Generator for Simulated Annealing in Mixed-Integer/Continuous Global Optimization

To reduce the well-known jamming problem in global optimization algorithms, we propose a new generator for the simulated annealing algorithm based on the idea of reflection. Furthermore, we give conditions under which the sequence of points generated by this simulated annealing algorithm converges in probability to the global optimum for mixed-integer/continuous global optimization problems. Finally, we present numerical results on some artificial test problems as well as on a composite structural design problem.     

A simulated annealing driven multi-start algorithm for bound constrained global optimization

A derivative-free simulated annealing driven multi-start algorithm for continuous global optimization is presented. We first propose a trial point generation scheme in continuous simulated annealing which eliminates the need for the gradient-based trial point generation. We then suitably embed the multi-start procedure within the simulated annealing algorithm. We modify the derivative-free pattern search method and use it as the local search in the multi-start procedure. We study the convergence properties of the algorithm and test its performance on a set of 50 problems. Numerical results are presented which show the robustness of the algorithm. Numerical comparisons with a gradient-based simulated annealing algorithm and three population-based global optimization algorithms show that the new algorithm could offer a reasonable alternative to many currently available global optimization algorithms, specially for problems requiring ‘direct search’ type algorithm.     

一类连续函数模拟退火算法及其收敛性分析

高维连续函数的全局优化问题普遍存在于计算生物学、计算化学等领域．针对这类问题和现有连续函数模拟退火算法的某些不足，本文给出了一类改进的模拟退火算法．采用一种简单的方法证明了算法的全局收敛性．数值结果表明，对于高维连续函数，该算法能够快速有效地收敛到全局最优点，比较了两种新解产生方法的试验结果。     

Molecular conformation on the CM-5 by parallel two-level simulated annealing

In this paper, we propose a new kind of simulated annealing algorithm calledtwo-level simulated annealing for solving certain class of hard combinatorial optimization problems. This two-level simulated annealing algorithm is less likely to get stuck at a non-global minimizer than conventional simulated annealing algorithms. We also propose a parallel version of our two-level simulated annealing algorithm and discuss its efficiency. This new technique is then applied to the Molecular Conformation problem in 3 dimensional Euclidean space. Extensive computational results on Thinking Machines CM-5 are presented. With the full Lennard-Jones potential function, we were able to get satisfactory results for problems for cluster sizes as large as 100,000. A peak rate of over 0.8 giga flop per second in 64-bit operations was sustained on a partition with 512 processing elements. To the best of our knowledge, ground states of Lennard-Jones clusters of size as large as these have never been reported before.Also a researcher at the Army High Performance Computing Research Center, University of Minnesota, Minneapolis, MN 55415     

Total completion time with makespan constraint in no-wait flowshops with setup times

The m-machine no-wait flowshop scheduling problem with the objective of minimizing total completion time subject to the constraint that the makespan value is not greater than a certain value is addressed in this paper. Setup times are considered non-zero values, and thus, setup times are treated as separate from processing times. Several recent algorithms, an insertion algorithm, two genetic algorithms, three simulated annealing algorithms, two cloud theory-based simulated annealing algorithms, and a differential evolution algorithm are adapted and proposed for the problem. An extensive computational analysis has been conducted for the evaluation of the proposed algorithms. The computational analysis indicates that one of the nine proposed algorithms, one of the simulated annealing algorithms (ISA-2), performs much better than the others under the same computational time. Moreover, the analysis indicates that the algorithm ISA-2 performs significantly better than the earlier existing best algorithm. Specifically, the best performing algorithm, ISA-2, proposed in this paper reduces the error of the existing best algorithm in the literature by at least 90% under the same computational time. All the results have been statistically tested.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.     

Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.     

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.