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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.


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%.


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.


Zheng H.,Guangxi University for Nationalities | Luo Q.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis
International Journal of Digital Content Technology and its Applications | Year: 2012

In this paper, aiming at the slow convergence of Cuckoo Search algorithm, we presents a novel hybrid optimization algorithm named SMCS incorporated Simplex Method (SM) into Cuckoo Search algorithm. It takes use of good global searching ability of CS and good local searching ability and fast convergence of SM, so that the convergence speed and solution precision of CS are improved. The experimental results show that the hybrid optimization algorithm is effective, and their performance excels those single optimization methods.


Ouyang X.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis | Luo Q.,Guangxi University for Nationalities | Chen H.,Guangxi University for Nationalities
Applied Mathematics and Information Sciences | Year: 2013

In this paper, we propose a novel discrete cuckoo search algorithm (DCS) for solving spherical Traveling Salesman Problem (TSP) where all points are on the surface of a sphere. The algorithm is based on the Lévy flight behaviour and brood parasitic behaviour. The proposed algorithm applies study operator, the "A" operator, and 3-opt operator to solutions in the bulletin board to speed up the convergence. Optimization results obtained for HA30 (an instance from TSPLIB) and different size problems are solved. Compared with GA, DCS is better and faster. © 2013 NSP Natural Sciences Publishing Cor.


Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis | Zhou G.,CAS Institute of Computing Technology | Zhang J.,Guangxi University for Nationalities
Applied Mathematics and Information Sciences | Year: 2013

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. Firstly, the presented algorithm embeds predatory behavior of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the improved GSO with differential evolution (DE) on the basis of a two-population co-evolution mechanism. Secondly, under the guidance of the feasibility rules, the swarm converges towards the feasible region quickly. In addition, to overcome premature convergence, the local search strategy based on simulated annealing (SA) is used and makesthe search near the true optimum solution gradually. Finally, the HGSO algorithm is for solving constrained engineering design problems. The results show that HGSO algorithm has faster convergence speed, higher computational precision, and is more effective for solving constrained engineering design problems. © 2013 NSP. Natural Sciences Publishing Cor.


Huang W.,Guangxi University for Nationalities | Huang W.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Applying affine transformations to polygon (concave or convex) and line segment, the algorithm of this paper obtains the points of intersection of the polygon and the line segment. Having tested the line segment with a bounding box which includes the polygon, the algorithm applies the affine transformation to the polygon and the line segment; in the polygon, the algorithm finds the edges which intersect the straight line. The algorithm applies another affine transformation to the edges, and obtains the points of intersection of the polygon and the line segment. Finally, The algorithm applies the reverse affine transformations to the points , and obtains the intersection points of the original polygon and the original line segment. © 2013 Springer-Verlag.


Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis | Chen H.,Guangxi University for Nationalities | Zhou G.,CAS Institute of Computing Technology
Neurocomputing | Year: 2014

In this paper, an invasive weed optimization (IWO) scheduling algorithm is presented for optimization no-idle flow-shop scheduling problem (NFSP) with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is put forward to calculate the makespan of job sequence. Secondly, the most position value (MPV) method is used to code the weed individuals so that fitness values can be calculated. Then, use the global exploration capacity of IWO to select the best fitness value and its corresponding processing sequence of job by evaluating the fitness of individuals. The results of 12 different scale NFSP benchmarks compared with other algorithms show that NFSP can be effectively solved by IWO with stronger robustness. © 2014 Elsevier B.V.


Li G.,Guangxi Teachers Education University | Li G.,Guangxi Key Laboratory of Hybrid Computation and IC design Analysis
Lecture Notes in Electrical Engineering | Year: 2015

Knowledge discovery from knowledge bases is an important problem in the field of data mining because it can solve the problem of a lack of knowledge, which is a bottleneck in intelligent systems based on knowledge bases. With the expansion of knowledge bases and structured data becoming more complex, first-order logic is no longer capable of knowledge representation and induction, so higher-order logic is naturally used in this case. In this paper, the process by which knowledge representation is adopted from first-order logic to higher-order logic is discussed, and a decision-tree algorithm learned with higher-order logic is also presented. Experimental results show that the proposed algorithm is efficient. © Springer International Publishing Switzerland 2015.


Liao W.-Z.,Jiaxing University | Liao W.-Z.,Guangxi Key laboratory of Hybrid Computation and IC Design Analysis
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2016

In order to improve the efficiency of test data generation for path coverage, a method for generating test data was proposed, which was based on automatic division of path and artificial fish-swarm (AFS) algorithm. Firstly, the relations between variables and nodes, and between variables and paths, were analyzed. Based on the analysis an algorithm for automatic division of path was presented, which can automatically judge the impact of variables on sub-paths. Secondly, an improved AFS algorithm was developed based on Levy flying and conjugate gradient. By making use of the result of path division and the improved AFS algorithm, a new method for searching test data was proposed. If there exist sub paths that the fish pass through in the process of using AFS to generate test data, the corresponding component of these fish were fixed, so that search space were reduced. Finally, the proposed method was applied to the test data generation of programs. It is shown that our method outperforms the related methods in running time, success rate and stability. © 2016, Chinese Institute of Electronics. All right reserved.

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