35156713
A novel ecological particle swarm optimization algorithm and its population dynamics analysis
This paper presents a novel particle swarm optimization algorithm from the angle of ecological population evolution, called the ecological particle swarm optimization, or EPSO. Initially, ecological population competition model (EPCM) is presented. From the basis of the EPCM, the EPSO algorithm and its general framework are proposed; in which particle swarm system with ecological hierarchy and competition model is defined and two collocating strategies of inertia weight factor are considered. The convergence performance and population dynamics including population aggregation and population diversity of the proposed approach are discussed separately through empirical simulations with well-known benchmarks from the standard literature.
30275519
Traffic Intelligent Optimization and Local Traffic-Flow Control Inside Shanghai-EXPO-Area
45515067
Evaluation Mode Research on Particle Swarm Optimization Algorithm
34391241
A turbo codes optimization method using particle swarm algorithm
Turbo Codes present a new direction for the channel encoding, especially since they were adopted for multiple norms of telecommunications, such as deeper communication, etc. To obtain an excellent performance, it is necessary to design robust turbo code interleaver and decoding algorithms. In this paper, we are investigating particle swarm algorithm as a promising optimization method to find good interleaver for the large frame sizes, as well as design the decoding optimization mode (PSO-Turbo); and apply the proposed PSO-Turbo codes mode to the security radio data transmission; in which, a kind of transport control proposal based on PSO-Turbo optimizer for CBTC wireless channel is designed and simulated to validate our method.
37911723
The Research of Complex System Modeling Based on Fractal Conception
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
15142417
Low-Complexity Analysis of Repetitive Regularities for Biometric Applications
Presented in this paper is a joint algorithm optimization and architecture design framework for analysis of repetitive regularities. Two closely coupled algorithm optimization techniques, referred to as the prime subspace periodicity transform (PSPT) and circular periodicity transform (CPT), are developed that significantly reduce computational complexity while enable the extraction of a wide spectrum of periodic features. The proposed PSPTCPT algorithms lead to a parallel and resource sharing VLSI architecture. While most of the current systems rely on software solutions to performance feature extraction, the performance benefits rendered by the proposed framework show advantages in dealing with data-intensive computation for emerging applications in biometrics and bioinformatics. The explosive growth in database complexity combined with demand for fast analysis make the proposed framework a promising solution. Experimental results on an iris identification system demonstrate up to 99.2% accuracy and 24.6% − 41.9% reduction in computational complexity.
14688887
Data Compression Algorithm Based on Hierarchical Cluster Model for Sensor Networks
A new distributed algorithm of data compression based on hierarchical cluster model for sensor networks is proposed, in which, the whole sensor network is mapped into a kind of hierarchical clusters model firstly, and then different wavelet transform models are used to commit data compression in inner and super clusters respectively. Theoretical analyses and simulation results show that, the above new methods have good performance of approximation, and can compress data and reduce the amount of data efficiently.
206594316
An OSEK COM Compliant Communication Model for Smart Vehicle Environment
Smart Vehicle Environment (SVE) is an important application of the idea of smart spaces. This paper presents Smart Vehicle Multi-Agent System (SVMAS) to achieve the goal of SVE and put forwards a commutation model for SVMAS based on SmartOSEK COM [1] to support data exchange. The paper also presents an approach to encapsulate the message to transport by CAN bus, and bring forward a simulator model for SVMAS. Finally the paper gives an example of the communication model which implements a dialogue between two agents and analyzes the performance. The contribution of our work is threefold. First, we adopt Knowledge Query and Manipulation Language (KQML) to describe the communication in vehicles. Second, we develop SmartOSEK COM to implement communication in vehicles. Third, we define the ACLcan protocol to transform the message from SmartOSEK COM to CAN frame.
15186113
Association based immune network for multimodal function optimization
For the problem of serious resources waste, indeterminate direction of local search and degeneration in the original opt-aiNet, a novel association based immune network is proposed for multimodal function optimization. The hexabasic model mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune association. The antibody population scale is semi-fixed reducing the time and space required to execute it. The information of the antibody population and the memory cells population is effective utilized to point out the direction of local search, to regulate the ratio between local search and global search, and to enhance the affinity of new antibodies. The elitist selection mechanism is adopted to ensure the convergence and stability of our algorithm respectively. The experiments on 10 benchmark functions show that when compared with opt-aiNet method, the new algorithm is capable of improving the search performance significantly in global convergence, convergence speed, computational cost, search ability, solution quality and algorithm stability.