An Improved Immune Evolutionary Algorithm for Multimodal Function Optimization
Based on the inspiration of immune system, a new multi-objective optimization algorithm is presented. The proposed approach adopts a cluster mechanism in order to divide the population into subpopulations for the stage of selection and reproduction. In the immune clonal selection process, a hybrid hypermutation operator is introduced to improve the variety of antibodies and affinity maturation, thus it can quickly obtain the global and local optima. The simulation results illustrated that the efficiency of the proposed algorithm for complicated function optimization and verified it's remarkable quality of the global and local convergence reliability.

title: The Predictive Control of Sintering Temperature in Rotary Kiln Based on Image Feedback and Soft Computing
abstract: The sintering temperature in rotary kiln is measured through image processing algorithm as feedback of its predictive control. The predictive model based on radial basis function (RBF) network is designed by the image and other process data optimized by genetic algorithm. The final results of application at an alumina sintering kiln show that this intelligent predictive control strategy can achieve higher stability in normal condition.

title: Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
abstract: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality. Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliers in high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional stream data is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the outlying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams. 
In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensional data streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set of top sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding the outlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliers for high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.

title: Do You Know Where Your Data's Been? - Tamper-Evident Database Provenance
abstract: Database provenance chronicles the history of updates and modifications to data, and has received much attention due to its central role in scientific data management. However, the use of provenance information still requires a leap of faith. Without additional protections, provenance records are vulnerable to accidental corruption, and even malicious forgery, a problem that is most pronounced in the loosely-coupled multi-user environments often found in scientific research. 
 
This paper investigates the problem of providing integrity and tamper-detection for database provenance. We propose a checksum-based approach, which is well-suited to the unique characteristics of database provenance, including non-linear provenance objects and provenance associated with multiple fine granularities of data. We demonstrate that the proposed solution satisfies a set of desirable security properties, and that the additional time and space overhead incurred by the checksum approach is manageable, making the solution feasible in practice.

title: Dynamic Equivalents of Power System Based on Extended Two Particle Swarm Optimization
abstract: A novel methodology based on phaser measurement units (PMUs) for power system dynamic equivalents is presented. In this methodology the external system is reduced dynamically using the important quantities obtained by PMUs. An extended two particle swarm optimization (PSO) algorithm with the mutation operator is proposed to identify the parameters of the equivalent system. The proposed method is demonstrated and compared with the original system using the 10 machines 39 buses New England test system. The comparison shows that the proposed method can preserve the dynamic properties of the original system and has great value in engineering application.

title: The Application of Cultural Algorithm in Load Balancing of Hybrid P2P Network
abstract: This paper analyses the defect of the designing model of load balancing in hybrid P2P network, and proposes a new strategy of scheduling for load balancing, which based on culture algorithm of multilayer belief spaces. According to the current load status of each super-node in the system, it can calculate its load weight of the next time section by using the cultural algorithm, and assign the current requests of login with weighted round-robin. The extracted knowledge which is got by selecting the best belief space from the multilayer belief space is used to optimize the constrains of the evolutionary, in order to accelerate the optimization process of the calculation of load weight. Simulation experiments show that the allocation of login request to each node is to be balance much faster through using of cultural algorithm based on multilayer belief spaces.

title: The transient solution of time-dependent M/M/1 queues
abstract: The transient behavior of time-dependent M/M/1 queues is studied. The boundary probability function pi /sub 0/(t), which is the probability that the queue is empty at time t, is shown with analyticity arguments to satisfy a Volterra-type integral equation. The boundary integral equation is derived, and a numerical algorithm is used to solve the integral equation and to find the expected queue size from pi /sub 0/(t). The approach can be applied to many other types of time-dependent queues. Examples are given.