An Intelligent Differential Evolution Algorithm for Designing Trading-Ratio System of Water Market
As a novel optimization technique, neural network based optimization has gained much attention and some applications during the past decade. To enhance the performance of Differential Evolution Algorithm (DEA), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, an intelligent Differential Evolution Algorithm (IDEA) is proposed by incorporating neural network based search behaviors into classic DEA. Firstly, DEA operators are used for exploration by updating individuals so as to maintain the diversity of population and speedup the search process. Secondly, a multi-layer feed-forward neural network is employed for local exploitation to avoid being trapped in local optima and improve the convergence of the IDEA. Simulation results and comparisons based on well-known benchmarks and optimal designing of trading-ratio system for water market demonstrate that the IDEA can effectively enhance the searching efficiency and greatly improve the searching quality.

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: Research on Architecture and Key Technology for Service-Oriented Workflow Performance Analysis
abstract: With the advent of SOA and Grid technology, the service has become the most important element of information systems. Because of the characteristic of service, the operation and performance management of workflow meet some new difficulties. Firstly a three-dimensional model of service is proposed. Then the characteristics of workflow in service-oriented environments are presented, based on which the workflow performance analysis architecture is described. As key technologies, workflow performance evaluation and analysis are discussed, including a multi-layer performance evaluation model and three kinds of performance analysis methods.

title: Particle swarm optimization for function optimization in noisy environment
abstract: As a novel evolutionary searching technique, particle swarm optimization (PSO) has gained wide research and effective applications in the field of function optimization. However, to the best of our knowledge, most studies based on PSO are aimed at deterministic optimization problems. In this paper, the performance of PSO for function optimization in noisy environment is investigated, and an effective hybrid PSO approach named PSOOHT is proposed. In the PSOOHT, the population-based search mechanism of PSO is applied for well exploration and exploitation, and the optimal computing budget allocation (OCBA) technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good particles. Meanwhile, hypothesis test (HT) is also applied in the hybrid approach to reserve good particles and to maintain the diversity of the swarm as well. Numerical simulations based on several well-known function benchmarks with noise are carried out, and the effect of noise magnitude is also investigated as well. The results and comparisons demonstrate the superiority of PSOOHT in terms of searching quality and robustness.

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: 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: Discovering Overlapping Communities of Named Entities
abstract: Although community discovery based on social network analysis has been studied extensively in the Web hyperlink environment, limited research has been done in the case of named entities in text documents. The co-occurrence of entities in documents usually implies some connections among them. Investigating such connections can reveal important patterns. In this paper, we mine communities among named entities in Web documents and text corpus. Most existing works on community discovery generate a partition of the entity network, assuming each entity belongs to one community. However, in the scenario of named entities, an entity may participate in several communities. For example, a person is in the communities of his/her family, colleagues, and friends. In this paper, we propose a novel technique to mine overlapping communities of named entities. This technique is based on triangle formation, expansion, and clustering with content similarity. Our experimental results show that the proposed technique is highly effective.

title: Hybrid Ant Colony Algorithm and Its Application on Function Optimization
abstract: A new hybrid ant colony algorithm was proposed. Firstly, weight factor was introduced to the binary ant colony algorithm, and then we obtained a new probability by combining probability model of Population based incremental learning (PBIL) with transfer probability of ants pheromone . The new population are produced by probability model of PBIL, transfer probability of ants pheromone and the probability of proposed algorithm so that population polymorphism is ensured and the optimal convergence rate and the ability of breaking away from the local minima are improved. Optimization simulation results based on the benchmark test functions show that the hybrid algorithm has higher convergence rate and stability than binary ant colony algorithm (BACA) and Population based incremental learning (PBIL).

title: Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images
abstract: Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: ''ROI generation step'' and ''ROI classification step''. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the ''classification checkpoints'' which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.

title: Scoring the Data Using Association Rules
abstract: In many data mining applications, the objective is to select data cases of a target class. For example, in direct marketing, marketers want to select likely buyers of a particular product for promotion. In such applications, it is often too difficult to predict who will definitely be in the target class (e.g., the buyer class) because the data used for modeling is often very noisy and has a highly imbalanced class distribution. Traditionally, classification systems are used to solve this problem. Instead of classifying each data case to a definite class (e.g., buyer or non-buyer), a classification system is modified to produce a class probability estimate (or a score) for the data case to indicate the likelihood that the data case belongs to the target class (e.g., the buyer class). However, existing classification systems only aim to find a subset of the regularities or rules that exist in data. This subset of rules only gives a partial picture of the domain. In this paper, we show that the target selection problem can be mapped to association rule mining to provide a more powerful solution to the problem. Since association rule mining aims to find all rules in data, it is thus able to give a complete picture of the underlying relationships in the domain. The complete set of rules enables us to assign a more accurate class probability estimate to each data case. This paper proposes an effective and efficient technique to compute class probability estimates using association rules. Experiment results using public domain data and real-life application data show that in general the new technique performs markedly better than the state-of-the-art classification system C4.5, boosted C4.5, and the Naïve Bayesian system.

title: Adaptive Resource Allocation in Multiuser OFDM System Based on Genetic Algorithm
abstract: Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network systems. The capacity of MU-OFDM can be maximized as long as subchannels and power are allocated adaptively, a new method based on genetic algorithm (GA) is proposed for adaptive power allocation in MU-OFDM system. Some proportional fairness constraints are also added to assure that each user can achieve a required data rate. Each subchannel to the user with the best channel-to-noise ratio has been assigned and an optimal power allocation algorithm based on GA has been proposed. The results demonstrate that the new method we proposed is optimal and efficient to resource allocation.