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Li L.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis
Neural Computing and Applications | Year: 2014

Bat algorithm is a recent optimization algorithm with quick convergence, but its population diversity can be limited in some applications. This paper presents a new bat algorithm based on complex-valued encoding where the real part and the imaginary part will be updated separately. This approach can increase the diversity of the population and expands the dimensions for denoting. The simulation results of fourteen benchmark test functions show that the proposed algorithm is effective and feasible. Compared to the real-valued bat algorithm or particle swarm optimization, the proposed algorithm can get high precision and can almost reach the theoretical value. © 2014, Springer-Verlag London. Source


Tang Z.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis
Journal of Intelligent Systems | Year: 2015

Uninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm's convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms. Source


Chen X.,Guangxi University for Nationalities | Zhou Y.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis | Luo Q.,Guangxi University for Nationalities
The Scientific World Journal | Year: 2014

Clustering is a popular data analysis and data mining technique. The k -means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k -means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. © 2014 Xin Chen et al. Source


Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis | Zhou Y.,Guangxi University for Nationalities | Zheng H.,Guangxi University for Nationalities | Luo Q.,Guangxi University for Nationalities | And 2 more authors.
Applied Mathematics and Information Sciences | Year: 2013

In this paper, we proposed an improved cuckoo search optimization (ICS) algorithm for solving planar graph coloringproblem. The improved cuckoo search optimization algorithm is consisting of the walking one strategy, swap and inversionstrategy and greedy strategy. The proposed improved cuckoo search optimization algorithm can solve the planar graph coloring problem using four-colors more efficiently and accurately. The experimental results show that we proposed improved cuckoo search optimization algorithm can get smaller average iterations and higher correction coloring rate. © 2013 NSP Natural Sciences Publishing Cor. Source


Liu J.,Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis | Liu J.,Guangxi University for Nationalities | Zhou Y.,Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis | Zhou Y.,Guangxi University for Nationalities | And 6 more authors.
Journal of Computational Information Systems | Year: 2011

Aiming at the glowworm swarm optimization algorithm is easy to fall into local optimization, having the low speed of convergence and low accuracy. In order to solve these problems, this article raised a conception of definite updating search domains. Using this method to make the position updating glowworm move closer to the best so that to improve the accuracy and speeding up convergence. Through eight typical functions testing, experiment results show that the proposed algorithm has strong global searching capability, and can effectively avoid precocious phenomenon, thus obviously improving the optimization global ability. © 2011 Binary Information Press. Source

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