Algorithms for Solving Lexicographic Multicriterion Problems on Matroids

Algorithms for lexicographic multicriterion problems on matroids and multicriterion problems solved by the concession method are described. The algorithms are based on the polynomial procedure of finding a sequence of the best solutions to an extremum problem.     

Some Approaches to Solving Inventory Control Problems

Several approaches to the solution of inventory control problems are considered. These approaches are designed to find optimal strategies, control parameters for a given strategy, and probability estimates of resource supply. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 101–106, July–August 2005.     

Optimization Parallelizing for Discrete Programming Problems

Parallelizing of calculations is considered when discrete optimization problems are solved. A version of parallelizing of copies of an algorithm is proposed and analyzed. The results of computational experiments are given.     

Current Approaches for Solving Over-Constrained Problems

We summarize existing approaches to model and solve overconstrained problems. These problems are usually formulated as combinatorial optimization problems, and different specific and generic formalisms are discussed, including the special case of multi-objective optimization. Regarding solving methods, both systematic and local search approaches are considered. Finally we review a number of case studies on overconstrained problems taken from the specialized literature.     

RESTART Technology for Solving Discrete Optimization Problems

The RESTART technology is proposed, which allows us to speed up the solution of discrete optimization problems.     

求解一类非线性整数规划的新方法

针对一类非线性整数规划问题，通过构造直接高散搜索方向提出了一种十分有效的算法。     

Genetic Programming Approaches for Solving Elliptic Partial Differential Equations

In this paper, we propose a technique based on genetic programming (GP) for meshfree solution of elliptic partial differential equations. We employ the least-squares collocation principle to define an appropriate objective function, which is optimized using GP. Two approaches are presented for the repair of the symbolic expression for the field variables evolved by the GP algorithm to ensure that the governing equations as well as the boundary conditions are satisfied. In the case of problems defined on geometrically simple domains, we augment the solution evolved by GP with additional terms, such that the boundary conditions are satisfied by construction. To satisfy the boundary conditions for geometrically irregular domains, we combine the GP model with a radial basis function network. We improve the computational efficiency and accuracy of both techniques with gradient boosting, a technique originally developed by the machine learning community. Numerical studies are presented for operator problems on regular and irregular boundaries to illustrate the performance of the proposed algorithms.      

求解混合流水车间调度问题的离散布谷鸟算法

为求解混合流水车间调度问题,提出一种离散布谷鸟算法。针对常规解码方法难以获得最优解的缺点,提出一种改进的解码方法,基于工件数与并行机数,按概率随机分配机器;根据标准布谷鸟算法中莱维飞行和巢寄生行为两种位置更新策略的核心思想,提出基于位置交叉和个体距离的离散莱维飞行,设计基于最优插入和最优交换的巢寄生策略。最后算例对比实验结果显示,采用基于改进解码方法的离散布谷鸟算法求解所得结果的平均值最小,验证了改进解码方法能提高解的质量;实例测试所得结果均获得了当前最优解,验证了离散布谷鸟算法求解该类问题的优越性。     

网络安全与局域网安全解决方案

本文针对网络安全的重要性,通过分析系统安全的结构,提出网络系统安全应具备的八项功能;本文还就网络分段,以交换式集成器代替共享式集成器和VLAN的实现等手段,探讨了局域网安全解决方案。     

A Robust Algorithm for Solving Nonlinear Programming Problems

In this paper, we introduce a new algorithm for solving nonlinear programming (NLP) problems. It is an extension of Guo's algorithm [1] which possesses enhanced capabilities for solving NLP problems. These capabilities include: a) extending the variable subspace, b) adding a search process over subspaces and normalized constraints, c) using an adaptive penalty function, and d) adding the ability to deal with integer NLP problems, 0-1 NLP problems, and mixed-integer NLP problems which have equality constraints. These four enhancements increase the capabilities of the algorithm to solve nonlinear programming problems in a more robust and universal way. This paper will present results of numerical experiments which show that the new algorithm is not only more robust and universal than its competitors, but also its performance level is higher than any others in the literature.     

Two Fuzzy Approaches for Solving Multiobjective Decision Problems

Zimmermann (1978) proposed a fuzzy method for solving multiple objectivedecision problems whose main difficulty is in specifying the membershipfunctions of the objectives. We have considered two different approaches toconstruct those membership functions that give rise to two algorithms forsolving linear multiobjective decision problems.     

A Neural Network for Solving Nonlinear Programming Problems

A neural network for solving convex nonlinear programming problems is proposed in this paper. The distinguishing features of the proposed network are that the primal and dual problems can be solved simultaneously, all necessary and sufficient optimality conditions are incorporated, and no penalty parameter is involved. Based on Lyapunov, LaSalle and set stability theories, we prove strictly an important theoretical result that, for an arbitrary initial point, the trajectory of the proposed network does converge to the set of its equilibrium points, regardless of whether a convex nonlinear programming problem has unique or infinitely many optimal solutions. Numerical simulation results also show that the proposed network is feasible and efficient. In addition, a general method for transforming non-linear programming problems into unconstrained problems is also proposed. ID="A1" Correspondence and offprint requests to: Dr Z Chen, Department of Electronic Engineering, Brunel University, Uxbridge, Middle-sex, UK     

一种求解数值优化问题的进化规划算法

针对进化规划算法收敛速度慢、容易早熟收敛等问题,提出一种基于探测变异的进化规划算法。该算法通过降维得到多个探测变异量,对个体进行探测变异,使个体始终向适应度好的方向进化,并利用自适应高斯变异标准差伸缩搜索空间,使个体跳出局部最优解。通过3个经典算例对其性能进行测试,实验结果证明该算法收敛速度快,求解质量高,可以解决早熟收敛等问题。     

Mathematical Programming Problems Solving by Functional Voxel Method

One of the approaches to mathematical programming problems solving by means of R-functional modeling is considered. Some functional voxel method tools are presented for the wide class of optimization problems computer solution using voxel algorithm of gradient descent. The illustrative examples of the computer calculations in comparison to non-computer solutions are given.     

结合增量与启发式搜索的多目标问题处理方法

提出了一种结合增量与启发式搜索的多目标问题处理方法,设计并实现了一个基于路径扩展方法的多目标增量启发式搜索系统.当问题搜索图中边的权重发生改变或添加删除节点时,该系统通过对搜索现场进行实时的更新,部分利用先前搜索保留的信息,从更新后的状态开始求解新的问题,从而提高了重搜索的效率.对gridworld标准测试样例进行了大量的系统测试,实验结果表明：结合增量与启发式搜索的处理方法能够有效地解决状态格局不断变化的一系列相似的多目标最短路径问题.     

基于非线性规划的微粒群算法研究

在工程中,寻求复杂的带约束条件的非线形规划的最优解一直是一个难点,需要一种功能强大的算法,而微粒群算法(PSO)有这样的特点.PSO的代码和参数很少,概念简单,实现容易,随机简便,收敛性良好.目前,PSO的参数的控制和调整主要依赖于经验确定,不同参数的选择可能导致算法收敛加快或变慢,甚至不收敛;另外,需要对PSO不断进行改进以得到良好的收敛性.为此,用PSO来求解带约束条件的非线形规划问题,通过仿真实验对参数进行适当调整和算法分析,并引入收缩因子对算法改进,研究其对收敛速度的影响.计算机仿真实验表明对参数进行合理的调整,可使收敛速度明显加快,且引入收缩因子对算法改进算法的有效改进后,收敛速度提高近四倍.