DE and NLP Based QPLS Algorithm
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: An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling
abstract: This paper proposes a hybrid algorithm based on particle swarm optimization (PSO) for a multiobjective permutation flow shop scheduling problem, which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. Not only does the proposed multiobjective algorithm (named MOPSO) apply the parallel evolution mechanism of PSO characterized by individual improvement, population cooperation, and competition to effectively perform exploration but it also utilizes several adaptive local search methods to perform exploitation. First, to make PSO suitable for solving scheduling problems, a ranked-order value (ROV) rule based on a random key technique to convert the continuous position values of particles to job permutations is presented. Second, a multiobjective local search based on the Nawaz-Enscore-Ham heuristic is applied to good solutions with a specified probability to enhance the exploitation ability. Third, to enrich the searching behavior and to avoid premature convergence, a multiobjective local search based on simulated annealing with multiple different neighborhoods is designed, and an adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood will be used. Due to the fusion of multiple different searching operations, good solutions approximating the real Pareto front can be obtained. In addition, MOPSO adopts a random weighted linear sum function to aggregate multiple objectives to a single one for solution evaluation and for guiding the evolution process in the multiobjective sense. Due to the randomness of weights, searching direction can be enriched, and solutions with good diversity can be obtained. Simulation results and comparisons based on a variety of instances demonstrate the effectiveness, efficiency, and robustness of the proposed hybrid algorithm.

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: 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: 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: 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: Multi-level organization and summarization of the discovered rules
abstract: Many existing data mining techniques often p roduce a large number of rules, which make it very difficult for manual inspection o f the rules to identify those interesting ones. This problem represents a major gap between the results of data mining and the understanding and use of the mining results. In this paper, we a rgue that t he key problem is not with the large number of rules because if there are indeed many rules that exist in data, they should b e discovered. The main p roblem is with ou r inability to organize, summarize and present the rules in such a way that they can b e ea sily analyzed b y the user. In this paper, we propose a technique to intuitively organize a nd summarize the discovered rules. With this organization, the discovered rules can b e presented to the user in the way as we think and talk about knowledge in ou r daily lives. This organization also allows the user to view the discovered rules at different levels of details, and to focus his/her attention on those interesting aspects. This paper presents this technique a nd u ses it t o o rganize, summarize a nd present t he knowledge e mbedded in a decision tree, and a set of association rules. Experiment results and p ractical applications show that the technique is both intuitive and 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: Intuitive Representation of Decision Trees Using General Rules and Exceptions
abstract: Producing too many rules is a major problem with many data mining techniques. This paper argues that one of the key reasons for the large number of rules is that an inefficient knowledge representation scheme has been used. The current predominant representation of the discovered knowledge is the if-then rules. This representation often severely fragments the knowledge that exists in the data, thereby resulting in a large number of rules. The fragmentation also makes the discovered rules hard to understand and to use. In this paper, we propose a more efficient representation scheme, called general rules & exceptions. In this representation, a unit of knowledge consists of a single general rule and a set of exceptions. This scheme reduces the complexity of the discovered knowledge substantially. It is also intuitive and easy to understand. This paper focuses on using the representation to express the knowledge embedded in a decision tree. An algorithm that converts a decision tree to the new representation is presented. Experiment results show that the new representation dramatically simplifies the decision tree. Real-life applications also confirm that this representation is more intuitive to human users.

title: A Reinforcement Approach to Schelduling

title: A weight based compact genetic algorithm
abstract: In order to improve the performance of the compact Genetic Algorithm (cGA) to solve difficult optimization problems, an improved cGA which named as the weight based compact Genetic Algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in each generation, when the winner competing with the other S-1 individuals to update the probability vector, different weights are multiplied to each solution according to the sequence of the solution ranked in the S-1 individuals. Experimental results on three kinds of Benchmark functions show that the proposed algorithm has higher optimal precision than that of the standard cGA and the cGA simulating higher selection pressures.