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Li Y.,Guangxi University for Nationalities | Li Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis
Journal of Computers (Finland) | Year: 2013

Quality of Service (QoS) anycast routing problem is a nonlinear combination optimization problem, which is proved to be a NP-complete problem, at present, the problem can be prevailingly solved by heuristic methods. Ant colony optimization algorithm (ACO) is a novel random search algorithm. On the one hand, it does not depend on the specific mathematical description, on the other hand, which has the advantages of robust, positive feedback, distributed computing. Consequently, ACO has been widely used in solving combinatorial optimization problems. However, the basic ACO has several shortcomings that the convergence rate is slow and it's easily to stuck in local optimum for solving QoS anycast routing problem. In this paper, the basic ACO has been improved, firstly, iteration operator is introduced in the node selection, which can make the node selection strategy is adjusted dynamically with the iteration. Secondly, pheromone evaporation coefficient is adjusted adaptively according to the distribution of ant colony. Finally, according to the evolutionary speed of the population, the premature convergence is estimated. The mutation and secondary ant colony operation is introduced, which can make the algorithm successfully to escape from local optima, and can rapidly approximate to the global optimum. Simulation results show that the algorithm has preferable global search ability and can effectively jump out of local optimum and rapidly converge to the global optimal solution. Thereby, the algorithm is feasible and effective. © 2013 ACADEMY PUBLISHER. Source

Chen H.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis | He S.,Guangxi University for Nationalities | And 2 more authors.
Journal of Computational and Theoretical Nanoscience | Year: 2013

An Invasive Weed Optimization (IWO) scheduling algorithm for solving Permutation Flow-shop Scheduling Problem (PFSP) is proposed. The Most Position Value (MPV) method is used to coding the weed individuals so that fitness values can be calculated. Then, the global exploration capacity of IWO is used to select the best fitness value and its corresponding processing sequence of job by evaluating the fitness of individuals. The results of 6 PFSP benchmarks compared with other algorithms show that PFSP can be effectively solved by IWO with adaptability and robustness. Copyright © 2013 American Scientific Publishers. All rights reserved. Source

Zheng H.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis
Journal of Computational Information Systems | Year: 2012

This paper according to the low convergence of rate of Cuckoo Search (CS) algorithm, a novel cuckoo search optimization algorithm base on Gauss distribution (GCS) is presented. We then apply the GCS algorithm to solve standard test functions and engineering design optimization problems, the optimal solutions obtained by GCS are far better than the best solutions obtained by CS, and the GCS has a high convergence rate. © 2011 by Binary Information Press. Source

Xuan S.,Guangxi University for Nationalities | Xuan S.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis
Mathematical Problems in Engineering | Year: 2015

Based on kernel principal component analysis, fuzzy set theory, and maximum margin criterion, a novel image feature extraction and recognition method, called fuzzy kernel maximum margin criterion (FKMMC), is proposed. In the proposed method, two new fuzzy scatter matrixes are redefined. The new fuzzy scatter matrix can reflect fully the relation between fuzzy membership degree and the offset of the training sample to subclass center. Besides, a concise reliable computational method of the fuzzy between-class scatter matrix is provided. Experimental results on four face databases (AR, extended Yale B, GTFD, and FERET) demonstrate that the proposed method outperforms other methods. Copyright © 2015 Shibin Xuan. Source

Zhou Y.-Q.,Guangxi University for Nationalities | Zhou Y.-Q.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis | Huang Z.-X.,Guangxi University for Nationalities | Huang Z.-X.,Youjiang Medical University for Nationalities | Liu H.-X.,Guangxi University for Nationalities
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2012

A discrete glowworm swarm optimization (DGSO) algorithm is designed to tackle the travelling salesman problem. A new encoding schema and decoding schema are given with the characteristics of the TSP problem, and a new distance formula and encoding update formula for the new algorithm are given. In order to enhance the capability of the algorithm local searching, and to speed up the algorithm convergence speed, the 2-opt local search scheme is integrated into the new algorithm for solving TSP problem. The proposed algorithm was evaluated on 10 TSP test problems. The numerical experiments show that the proposed algorithm can find the global optimal solution with less computation and evolving time. In case of large scale TSP algorithm can achieve optimal solution of the theory and the error of the optimal solution is also less than 1%. Source

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