Improved Differential Evolution with Dynamic Population Size
As a novel evolutionary computing technique, recently Differential Evolution (DE) has attracted much attention and wide applications due to its simple concept and easy implementation. However, all the control parameters of the classic DE (crossover rate, scaling factor, and population size) keep fixed during the searching process. To improve the performance of DE, an improved DE (IDE) with dynamic population size is proposed in this paper. Simulation results and comparisons based on some well-known benchmarks and an IIR design problem show the good efficiency of the proposed IDE.

title: Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
abstract: A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.

title: A New Performance Evaluation Model and AHP-Based Analysis Method in Service-Oriented Workflow
abstract: In service-oriented architecture, services and workflows are closely related so that the research on service-oriented workflow attracts the attention of academia. Because of the loosely-coupled, autonomic and dynamic nature of service, the operation and performance evaluation of workflow meet some challenges, such as how to judge the quality of service (QoS) and what is the relation between QoS and workflow performance. In this paper we are going to address these challenges. First the definition of service is proposed, and the characteristics and operation mechanism of service-oriented workflow are presented. Then a service-oriented workflow performance evaluation model is described which combines the performance of the business system and IT system. The key performance indicators (KPI) are also depicted with their formal representation. Finally the improved Analytic Hierarchy Process is brought forward to analyze the correlation between different KPIs and select services.

title: DE and NLP Based QPLS Algorithm
abstract: As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

title: Manufacturing Grid: Needs, Concept, and Architecture
abstract: As a new approach, grid technology is rapidly used in scientific computing, large-scale data management, and collaborative work. But in the field of manufacturing, the application of grid is just at the beginning. The paper proposes the concept of manufacturing. The needs, definition and architecture of manufacturing gird are discussed, which explains why needs manufacturing grid, what is manufacturing grid and how to construct a manufacturing grid system.

title: An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
abstract: This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed

title: Mining Comparative Sentences and Relations
abstract: This paper studies a text mining problem, comparative sentence mining (CSM). A comparative sentence expresses an ordering relation between two sets of entities with respect to some common features. For example, the comparative sentence "Canon's optics are better than those of Sony and Nikon" expresses the comparative relation: (better, {optics}, {Canon}, {Sony, Nikon}). Given a set of evaluative texts on the Web, e.g., reviews, forum postings, and news articles, the task of comparative sentence mining is (1) to identify comparative sentences from the texts and (2) to extract comparative relations from the identified comparative sentences. This problem has many applications. For example, a product manufacturer wants to know customer opinions of its products in comparison with those of its competitors. In this paper, we propose two novel techniques based on two new types of sequential rules to perform the tasks. Experimental evaluation has been conducted using different types of evaluative texts from the Web. Results show that our techniques are very promising.

title: Solvability of multi-point boundary value problem at resonance--Part IV

title: Post-Analysis of Learned Rules
abstract: Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life applications, the situation may not be as simple as that, particularly, when the user of the rules is a human being. The human user almost always has some previous concepts or knowledge about the domain represented by the dataset. Naturally, he/she wishes to know how the new rules compare with his/her existing knowledge. In dynamic domains where the rules may change over time, it is important to know what the changes are. These aspects of research have largely been ignored in the past. With the increasing use of machine leaming tcclmiques in practical applications such as data mining, this issue of post analysis of rules warrants greater emphasis and attention. In this paper, we propose a technique to deal with this problem. A system has been implemented to perform the post analysis of classification rules genemted by systems such as C4.5. The proposed technique is general and highly interactive. It will be particularly useful in data mining and data analysis.

title: Noise-Analysis Based Threshold-Choosing Algorithm in Motion Estimation
abstract: A novel threshold choosing method for the threshold-based skip mechanism is presented, in which the threshold is obtained from the analysis of the video device induced noise variance. Simulation results show that the proposed method can remarkably reduce the computation time consumption with only marginal performance penalty.

title: Binary Tree Support Vector Machine Based on Kernel Fisher Discriminant for Multi-classification
abstract: In order to improve the accuracy of the conventional algorithms for multi-classifications, we propose a binary tree support vector machine based on Kernel Fisher Discriminant in this paper. To examine the training accuracy and the generalization performance of the proposed algorithm, One-against-All, One-against-One and the proposed algorithms are applied to five UCI data sets. The experimental results show that in general, the training and the testing accuracy of the proposed algorithm is the best one, and there exist no unclassifiable regions in the proposed algorithm.