网格资源部署动态策略的一种拟生算法

通过将资源动态部署问题转化成收益目标函数的优化问题，并在网格架构基础上，通过模拟生物跟随食物变化的动态分布过程，引入服务节点的动态迁移、竞争和死亡等行为，设计了拟生动态部署优化策略解决该优化问题．在针对单一资源访问以延时为主要服务质量评价标准的测试过程中拟生部署策略的目标函数值相比传统方法均有提高，算法时间复杂度接近O(N^2)，各个计算节点时间复杂度接近O(N)，算法的实际叠代次数约为2～3次．     

Attribute specification for finite element models

Two different approaches for the application of problem specific attributes to finite element models are discussed. In both approaches interactive computer graphics techniques and the use of a top-down program approach is emphasized. The major difference in the approaches is the point during problem definition that the attributes are applied and manifests itself primarily in the program's database with little effect on program operation. Program operation is demonstrated through the use of an example problem. Finally, the advantages and disadvantages of the two approaches are discussed.     

Dual unification of bi-class support vector machine formulations

Support vector machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the linearly separable case and that all the solutions are equivalent. For the non-linearly separable case, all the approaches can also be formulated as a unique dual optimization problem, however, their solutions are not equivalent. Discussions and remarks in the article point to an in-depth comparison between SVM formulations and associated parameters.     

A study of concept-based similarity approaches for recommending program examples

This paper investigates a range of concept-based example recommendation approaches that we developed to provide example-based problem-solving support in the domain of programming. The goal of these approaches is to offer students a set of most relevant remedial examples when they have trouble solving a code comprehension problem where students examine a program code to determine its output or the final value of a variable. In this paper, we use the ideas of semantic-level similarity-based linking developed in the area of intelligent hypertext to generate examples for the given problem. To determine the best-performing approach, we explored two groups of similarity approaches for selecting examples: non-structural approaches focusing on examples that are similar to the problem in terms of concept coverage and structural approaches focusing on examples that are similar to the problem by the structure of the content. We also explored the value of personalized example recommendation based on student's knowledge levels and learning goal of the exercise. The paper presents concept-based similarity approaches that we developed, explains the data collection studies and reports the result of comparative analysis. The results of our analysis showed better ranking performance of the personalized structural variant of cosine similarity approach.     

On exact solutions for the Minmax Regret Spanning Tree problem

The Minmax Regret Spanning Tree problem is studied in this paper. This is a generalization of the well-known Minimum Spanning Tree problem, which considers uncertainty in the cost function. Particularly, it is assumed that the cost parameter associated with each edge is an interval whose lower and upper limits are known, and the Minmax Regret is the optimization criterion. The Minmax Regret Spanning Tree problem is an NP-Hard optimization problem for which exact and heuristic approaches have been proposed. Several exact algorithms are proposed and computationally compared with the most effective approaches of the literature. It is shown that a proposed branch-and-cut approach outperforms the previous approaches when considering several classes of instances from the literature.     

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered learning-based optimization (LBO) algorithms to solve the VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches. We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms. Finally, we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.      

A New Method for Reasoning about Action

Reasoning about action is an important aspect of common sense reasoning and planning.It gives rise to three classical problems:the frame problem,the qualification problem and the ramification problem.Existing approaches cannot deal with these problems efficiently.This paper presents a new method which uses the stratified ATMS for reasoning about action to overcome the limitations of these approaches.     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

Ensuring portability of software

The paper is devoted to the problem of portability of applications between different software-hardware platforms. A survey of approaches to solving this problem is given, and analysis of their advantages and disadvantages is presented. Application domains of the existing approaches are discussed.     

Solution of output regulation of singular nonlinear systems bynormal output feedback

The output regulation problem of singular nonlinear systems via the normal output feedback control has been a challenging problem. Existing approaches for solving this problem employed techniques similar to those used for linear singular systems. Results from these approaches either rely on a normalizability assumption or are limited to systems with special structures. This paper gives a complete solution for this problem by employing a novel approach that is also interesting for linear systems