不等式约束最优化的一个使用非单调搜索的可行算法

对不等式约束最优化问题本文提出了一个新算法。算法使用了非单调搜索,它不仅放松了每步迭代中对搜索的限制,而且使得算法迭代到一定阶段后具有非常简洁的形式,在不需要严格互补条件的较弱假设下,算法是整体和超线性收敛的。     

Transverse dynamics of space charge dominated beam during bunching

In this paper we present a technique to optimize the transport parameters for transverse matching of the space charge dominated beams in the presence of bunching. The increase in the current within the specified bunch width during the transport is obtained from the disc model and is included in the optimization method. A 2D particle in cell (PIC) code is then developed to study the details transverse dynamics of the bunch taking into account the effect of longitudinal compression by reweighting the charge and mass of macroparticles. The evolution of rms beam size obtained using PIC simulation agrees well with the results obtained by solving beam envelope equation. The PIC simulation shows emittance growth during bunching induced by the space charge effect for nonuniform distribution. The growth in the emittance increases with the nonlinearity of distribution and peaks near the time focus.     

Parallelizing Tabu Search on a Cluster of Heterogeneous Workstations

In this paper, we present the parallelization of tabu search on a network of workstations using PVM. Two parallelization strategies are integrated: functional decomposition strategy and multi-search threads strategy. In addition, domain decomposition strategy is implemented probabilistically. The performance of each strategy is observed and analyzed. The goal of parallelization is to speedup the search in finding better quality solutions. Observations support that both parallelization strategies are beneficial, with functional decomposition producing slightly better results. Experiments were conducted for the VLSI cell placement, an NP-hard problem, and the objective was to achieve the best possible solution in terms of interconnection length, timing performance (circuit speed), and area. The multiobjective nature of this problem is addressed using a fuzzy goal-based cost computation.     

Efficient Heuristics for the Heterogeneous Fleet Multitrip VRP with Application to a Large-Scale Real Case

The basic Vehicle Routing Problem (VRP) consists of computing a set of trips of minimum total cost, to deliver fixed amounts of goods to customers with a fleet of identical vehicles. Few papers address the case with several types of vehicles (heterogeneous fleet). Most of them assume an unlimited number of vehicles of each type, to dimension the fleet from a strategic point of view. This paper tackles the more realistic tactical or operational case, with a fixed number of vehicles of each type, and the optional possibility for each vehicle to perform several trips. It describes several heuristics, including a very efficient one that progressively merges small starting trips, while ensuring that they can be performed by the fleet. This heuristic seeks to minimize the number of required vehicles as a secondary objective. It outperforms classical VRP heuristics, can easily handle various constraints, and gives very good initial solutions for a tabu search method. The real case of a French manufacturer of furniture with 775 destination stores is presented.     

二冷配水优化建模与混合自适应PSO算法求解

针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题。依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法。由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果。研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力。将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题。该方法用于连铸二冷水优化是可行的、有效的。     

Progressive hedging and tabu search applied to mixed integer (0,1) multistage stochastic programming

Many problems faced by decision makers are characterized by a multistage decision process with uncertainty about the future and some decisions constrained to take on values of either zero or one (for example, either open a facility at a location or do not open it). Although some mathematical theory exists concerning such problems, no general-purpose algorithms have been available to address them. In this article, we introduce the first implementation of general purpose methods for finding good solutions to multistage, stochastic mixed-integer (0, 1) programming problems. The solution method makes use of Rockafellar and Wets' progressive hedging algorithm that averages solutions rather than data. Solutions to the induced quadratic (0,1) mixed-integer subproblems are obtained using a tabu search algorithm. We introduce the notion of integer convergence for progressive hedging. Computational experiments verify that the method is effective. The software that we have developed reads standard (SMPS) data files.     

最佳粮库地址的选择

管理部门通常要选择适当的地方建造粮库 ,所需服务范围已知 ,各部门运输量给定 .需为他们选择合适的地方 ,使总运费最少 .某乡的九个村 (A,B,C,… ,H,I)如图 1 ,各村距离给出 ,并标明它们各自上缴公粮数 .管理部门希望在村内或道路上建立一个粮库 ,最大限度地减少运输费用 .问题的解法有几种方案 ,对于本题来说 ,穷举搜索法是可行的 .另外 ,我们提出一种分析求解法 ,可找到优化解 .它利用图论的基础知识先求出图 1的各顶点间的最小路径 ,再进一步求出图的绝对中心 (即粮库的地址 ) ,其中的有关计算利用了 C++语言程序 .在此基础上 ,还可对问题的参数作更精细的分析 .概括地说 ,穷举搜索法对于简单的区域是行之有效的 .但对于更加一般化的问题 ,利用计算机可快捷准确地得到答案 .通过建立模型 ,我们得到下面两个结论 :(1 )我们找到最优解是 E点 ,其总运费为 1 2 775元 .(2 )模型具有广泛性 ,对于更一般的区域 ,可利用计算机总可以求出最优解 .     

一种基于GIS的选址问题及算法

本文考虑了选址区域内存在地理阻断情况下的一种基于GIS的选址问题.对单配送中心选址模型,以GIS返回的任意两点间的最短可行路径的长度作为修正距离函数,分析了目标函数在凸形选址区域上非凸非连续的性质.进一步,采用给出了一种近似搜索算法并通过一个实例计算与重心法进行了比较.     

无记忆非拟 Newton 算法的导出和全局收敛性

In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method. The global convergence of this hybrid memoryless method is proved under mild assumptions. The initial results show that these new methods are efficient for the given test problems. Especially the memoryless non-quasi-Newton method requires little storage and computation, so it is able to efficiently solve large scale optimization problems.     

General Quantum Meet-in-the-Middle Search Algorithm Based on Target Solution of Fixed Weight

Similar to the classical meet-in-the-middle algorithm, the storage and computation complexity are the key factors that decide the efficiency of the quantum meet-in-the-middle algorithm. Aiming at the target vector of fixed weight, based on the quantum meet-in-the-middle algorithm, the algorithm for searching all n-product vectors with the same weight is presented, whose complexity is better than the exhaustive search algorithm. And the algorithm can reduce the storage complexity of the quantum meet-in-the-middle search algorithm. Then based on the algorithm and the knapsack vector of the Chor-Rivest public-key crypto of fixed weight d, we present a general quantum meet-in-the-middle search algorithm based on the target solution of fixed weight, whose computational complexity is ∑_j^d=(0(√C_n-k+1^d-j)+O(C_k^jlogC_k^j)) with ∑_i=0^dC_kⁱ memory cost. And the optimal value of k is given. Compared to the quantum meet-in-the-middle search algorithm for knapsack problem and the quantum algorithm for searching a target solution of fixed weight, the computational complexity of the algorithm is lower. And its storage complexity is smaller than the quantum meet-in-the-middle-algorithm.