Qing Shuai

Particle Model to Optimize Enterprise Computing

This paper presents a novel generalized particle model (GPM) for the parallel optimization of enterprise computing. Since enterprise computing always involves the resource allocation, task assignment, and behavior coordination, without loss of generality, the proposed GPM is devoted to the optimization of enterprise computing in the context of the resource allocation and task assignment in complex environment. GPM transforms the optimization of enterprise computing into the kinematics and dynamics of massive particles in a force-field. The GPM approach has many advantages in terms of the high-scale parallelism, multi-objective optimization, multi-type coordination, multi-degree personality, and the ability to handle complex factors. Simulations have shown the effectiveness and suitability of the proposed GPM approach to optimize the enterprise computing.

Particle model to optimize resource allocation and task assignment

This paper presents a novel generalized particle model for the parallel optimization of the resource allocation and task assignment in complex environment of enterprise computing. The generalized particle model (GPM) transforms the optimization process into the kinematics and dynamics of massive particles in a force-field. The GPM approach has many advantages in terms of the high-scale parallelism, multi-objective optimization, multi-type coordination, multi-degree personality, and the ability to handle complex factors. Simulations show the effectiveness and suitability of the proposed GPM approach to optimize the enterprise computing.

Problem-Solving in Multi-Agent Systems: A Novel Generalized Particle Model

This paper presents a novel generalized particle model (GPM) for problem-solving in multi-agent systems (MAS). The construction, dynamics and properties of the GPA and corresponding algorithm are discussed. The GPA has many advantages in terms of the high-scale parallelism, multi-objective optimization, multi-type coordination, multi-degree autonomy, and the ability to deal randomly occurring phenomena in MAS systems

Generalized Cellular Automata For Data Clustering

This paper is devoted to novel stochastic generalized cellular automata (GCA) for self-organizing data clustering. The GCA transforms the data clustering process into a stochastic process over the configuration space in the GCA array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, the learning ability, and the easier hardware implementation with the VLSI systolic technology. The simulations and comparisons have shown the effectiveness and good performance of the proposed GCA approach to data clustering

Optimal Control of Network Services Based on Generalized Particle Model

The bandwidth allocation problem in ATM networks is NP-complete. This paper presents a novel generalized particle approach (GPA) to optimize the bandwidth allocation and QoS parameter for ATM networks. The GPA transforms the optimization of ATM networks into a kinematics and dynamics of numerous particles in a force-field. The GPA has many advantages in terms of the higher parallelism, multi-objective optimization, multi-type coordination, and easiness for hardware implementation. During the ATM networks optimization, the GPA may deal with a variety of random and emergent phenomena, such as the congestion, failure, and interaction. This paper also gives the GPAs properties regarding its correctness, convergency and stability. The simulations have shown the effectiveness and suitability of the GPA to the optimization of ATM networks

A Novel Quantum Particle Approach to Self-Organizing Clustering

Most of currently used approaches to data clustering are not qualified to quickly cluster a high-dimensional large-scale database. This paper is devoted to a novel generalized quantum particle model (GQPM) to data self-organizing clustering. The GQPM approach transforms the data clustering process into a stochastic process of particle motion, collision and quantum entanglement on a particle array. In comparison with the GPM clustering method we have proposed before, the GQPM has much faster speed and higher quality for clustering. GQPM is also characterized by the self-organizing clustering and has advantages in terms of the insensitivity to noise, the quality robustness to clustered data, the learning ability, the suitability for high-dimensional multi-shape large-scale data sets. The simulations and comparisons have shown the effectiveness and good performance of the proposed GQPM approach to data clustering

Particle Dynamics Approach to Multi-Agent Systems

The resources allocation and task assignment in complex distributed network environment is a typical problem of multi-agent systems (MAS). Even without taking into account interactions, coordinations, and a variety of random phenomena in networks, the bandwidth allocation problem in ATM networks is also NP-complete. This paper presents a particle dynamics approach (GPDA) that transforms the MAS problem-solving into the kinematics and dynamics of particles in a force-field. As an important application for problem-solving in MAS, this paper uses GPDA to optimize the bandwidth allocation and QoS in ATM networks. The GPA has features in terms of the high-degree parallelism, multi-objective optimization, multi-type coordination, multi-granularity coalition, and easier hardware implementation. Simulations and comparisons show the effectiveness and suitability of GPDA.

The Circuital Design of Generalized Cellular Automata for Parallel Optimization

The generalized cellular automata (GCA) has the pyramid architecture and the multi-granularity cellular dynamics for effectively solving a class of optimizations problems. In order to further take advantages of GCA, this paper discusses the hardware implementation of GCA with VLSI systolic techniques. In comparison with the Hopfield-type neural networks and cellular neural networks, the implementation scheme of GCA has features in terms of the much less number of interconnections, the higher-degree optimality, the quicker convergence speed, and the much easier selection of circuital parameters.

A Novel Stochastic Generalized Cellular Automata For Self-Organizing Data Clustering

This paper is devoted to novel stochastic generalized cellular automata (GCA) for self-organizing data clustering. The GCA transforms the data clustering process into a stochastic process over the configuration space in the GCA array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, the learning ability, and the easier hardware implementation with the VLSI systolic technology. The simulations and comparisons have shown the effectiveness and good performance of the proposed GCA approach to data clustering

Stochastic Particle Model for Service Self-Organization

This paper presents a self-organizing service modeling based on generalized particle dynamics (GPD) for distributed service systems. Differing from traditional service models, the GPD-based self-organizing service modeling may provide optimal services according to user requirements. The GPD conception, algorithm and its properties are discussed. The GPD-based self-organizing service modeling has advantages in terms of the real-time performance, adaptability, reliability and the learning ability over traditional service models