Solving a class of multiplicative programming problems via c-programming

In this note we show that many classes of global optimization problems can be treated most satisfactorily by classical optimization theory and conventional algorithms. We focus on the class of problems involving the minimization of the product of several convex functions on a convex set which was studied recently by Kunoet al. [3]. It is shown that these problems are typical composite concave programming problems and thus can be handled elegantly by c-programming [4]–[8] and its techniques.     

工程项目结构分解的优化及应用

工程项目结构分解及其编码是项目管理的基础工作。目前该方法的应用缺乏统一的标准和规范，影响其应用效果。针对这种情况，提出该方法的优化途径－－分解对象标准化，分解步骤统一化，分解规则规范化。进而指出该方法新的应用领域，特别是在项目管理信息集成方面，及对提高工程项目管理水平所起的作用。     

Pattern Search Method for Discrete <Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript>–Approximation

We propose a pattern search method to solve a classical nonsmooth optimization problem. In a deep analogy with pattern search methods for linear constrained optimization, the set of search directions at each iteration is defined in such a way that it conforms to the local geometry of the set of points of nondifferentiability near the current iterate. This is crucial to ensure convergence. The approach presented here can be extended to wider classes of nonsmooth optimization problems. Numerical experiments seem to be encouraging. This work was supported by M.U.R.S.T., Rome, Italy.     

Constraint incorporation in optimization

Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests; the incorporation needs to be simple enough that the solution method is tractable, yet complex enough to ensure the validity of the ultimate solution. We introduce a framework for constraint incorporation that identifies a minimal acceptable level of complexity and defines two basic types of constraint incorporation which (with combinations) cover nearly all popular numerical methods for constrained optimization, including trust region methods, penalty methods, barrier methods, penalty-multiplier methods, and sequential quadratic programming methods. The broad application of our framework relies on addition and chain rules for constraint incorporation which we develop here.     

Protein Conformation of a Lattice Model Using Tabu Search

We apply tabu search techniques to the problem of determining the optimal configuration of a chain of protein sequences on a cubic lattice. The problem under study is difficult to solve because of the large number of possible conformations and enormous amount of computations required. Tabu search is an iterative heuristic procedure which has been shown to be a remarkably effective method for solving combinatorial optimization problems. In this paper, an algorithm is designed for the cubic lattice model using tabu search. The algorithm has been tested on a chain of 27 monomers. Computational results show that our method outperforms previously reported approaches for the same model.     

变速齿轮传动系统最佳轴向结构布局智能设计的推理机制

本文通过对变速齿轮传动系统轴向结构布局设计规律的研究,提出了一种适合于该类问题的知识表达方式,将改进后的带有启发函数的最佳优先搜索方法引入到该问题的求解中,使得含有多重搜索空间以及带有一定约束的设计问题统一在同一搜索策略下。根据本文提出的方法,用PROLOG语言建造的推理机,通过对实际问题的考证,证明该方法在解决传动结构自动设计上是行之有效的,并且在推理机制上成功地体现了设计过程中反复设计与优化设计的功能特征.     

草甘膦中间体亚氨基二乙酸合成的最优化研究

本文用计算机辅助优化的方法对亚胺基二乙酸的合成条件,包括合成中所用材料,温度和溶剂用量等进行优化,同时对副产物甘氨酸的收率进行回归分析,最后确定合成该中间体的最优化条件。     

聚醚醚酮链结构与反应的模型化合物的量子化学研究

利用ＡＭ１方法对聚醚醚酮模型化合物全优化，结果为：芳环平均相互扭转角为３３．０度，桥键角１１７．０－１１８．０度，其分子结构拓扑图形表明：所有苯环（核）为平面构型，但其内角扭曲；其氢原子对苯环构型无实质性贡献，在更长链的计算中，冻结苯核与氢原子也得出满意结果，根据Ｍｕｌｌｉｋｅｎ键序与电荷讨论了醚交换与磺化反应。     

A New Integrated Design Method Based on Fuzzy Matter-Element Optimization

This paper puts forward a new integrated design met ho d based on fuzzy matter-element optimization.On the based of analyzing the mod el of multi-objective fuzzy matter-element , the paper defines the m atter-element weightily and changes solving multi-objective fuzzy optimization into solving dependent function K(x) of the single-objective optimization according to the optimization criterion. The paper particularly describes the realization approach of GA process of multi -objective fuzzy matter-eleme...     

Combinatorial Optimization by Dynamic Contraction

A heuristic optimization methodology, Dynamic Contraction (DC), is introduced as an approach for solving a wide variety of hard combinatorial problems. Contraction is an operation that maps an instance of a problem to a smaller instance of the same problem. DC is an iterative improvement strategy that relies on contraction as a mechanism for escaping local minima. As a byproduct of contraction, efficiency is improved due to a reduction of problem size. Effectiveness of DC is shown through simple applications to two classical combinatorial problems: The graph bisection problem and the traveling salesman problem.