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Wang H.,Nanchang Institute of Technology | Wang H.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing | Cui Z.,Taiyuan University of Science and Technology | Sun H.,Nanchang Institute of Technology | And 3 more authors.
Soft Computing | Year: 2016

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants. © 2016 Springer-Verlag Berlin Heidelberg Source


Wang H.,Nanchang Institute of Technology | Wang H.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing | Wang W.,Nanchang Institute of Technology | Sun H.,Nanchang Institute of Technology | Sun H.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing
International Journal of Wireless and Mobile Computing | Year: 2015

Firefly Algorithm (FA) is a new optimisation algorithm based on swarm intelligence, which has shown good performance on many optimisation problems. However, the standard FA easily falls into local minima because of too many attraction operations. To enhance the performance of the standard FA, a new FA is proposed in this paper. The new approach employs Generalised Opposition-Based Learning (GOBL) for population initialisation and generation jumping. To verify the performance of our approach, a set of benchmark functions tested in the experiments. Computational results show that the proposed approach obtains better performance than the standard FA and some recently proposed FA variants. © Copyright 2015 Inderscience Enterprises Ltd. Source


Wang H.,Nanchang Institute of Technology | Wang H.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing | Wang W.,Nanchang Institute of Technology | Sun H.,Nanchang Institute of Technology | And 7 more authors.
Communications in Computer and Information Science | Year: 2016

Firefly algorithm (FA) is a recently proposed swarm intelligence optimization technique, which has shown good performance on many optimization problems. In the standard FA and its most variants, a firefly moves to other brighter fireflies. If the current firefly is brighter than another one, the current one will not be conducted any search. In this paper, we propose a new firefly algorithm (called NFA) to address this issue. In NFA, brighter fireflies can move to other positions based on local search. To verify the performance of NFA, thirteen classical benchmark functions are tested. Experimental results show that our NFA outperforms the standard FA and two other modified FAs. © Springer Science+Business Media Singapore 2016. Source


Sun L.,Wuhan University | Sun L.,Zhengzhou Institute of Surveying and Mapping | Liu Z.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing | Liu Z.,Nanchang Institute of Technology | And 8 more authors.
Atmospheric Science Letters | Year: 2016

To analyze the dynamics of air pollution, a homogenous partition of the coarse graining process is employed to transform the daily air pollution index series in Lanzhou into a character series consisting of five characters (R, r, e, d and D). The nodes of the pollution fluctuation network are 125 three-symbol strings (i.e. 125 fluctuation patterns in a duration of 3 days) linked in the network's topology by a time sequence. The network contains integrated information about the interconnections and interactions among the fluctuation patterns of pollution in the network topology. After calculating the dynamical statistics of degree and degree distribution, we find that the distribution follows a three-stage power-law distribution characterized by a scale-free property with hierarchy structure and small-world effect. Therefore, the pollution fluctuation network is not only a scale-free network with hierarchy but also a small-world network. The higher the degree of the node is, the greater the probability that the pollution fluctuation modes will occur. The main nodes of pollution fluctuation networks generally contain the symbols R and r, which demonstrates that the feature of pollution fluctuation is mainly ascending. © 2016 Royal Meteorological Society. Source


Sun H.,Nanchang Institute of Technology | Sun H.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing | Li B.,Nanchang Institute of Technology | Li B.,Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing | And 2 more authors.
International Journal of Wireless and Mobile Computing | Year: 2015

In order to overcome the drawbacks of standard artificial bee colony (ABC) algorithm, such as slow convergence and low solution accuracy, a hybrid ABC algorithm based on different search mechanisms is proposed in this paper. According to the type of position information in ABC, three basic search mechanisms are summarised which include searching around the individual, the random neighbour, and the global best solution. Then, the basic search mechanisms are improved to obtain three search strategies. All of these strategies can make a good balance between exploration and exploitation. At every iteration, each bee randomly selects a search strategy to produce a candidate solution under the same probability. The experiment is conducted on 12 classical functions and 28 CEC2013 functions. Results show that the new algorithm performs significantly better than several recently proposed similar algorithms in terms of the convergence speed and solution accuracy. © Copyright 2015 Inderscience Enterprises Ltd. Source

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