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1st International Workshop on Education Technology and Computer Science

作者： Liu, Chun-bo Wang, Hui-jin Luo, Zhi-ping Yu, Xiu-qin Liu, Li-hua Jinan Univ Dept Comp Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China S China Univ Technol Comp & Engn Lab SDII Guangzhou 510006 Peoples R China Jinan Univ Coll Business Dept Stat Guangzhou 510632 Peoples R China Jinggangshan Univ Dept Educ Jian 343009 Jiangxi Peoples R China

ISBN: (纸本)9780769535579

Bandwidth-delay-constrained multicast routing problem is an NP-complete problem. In this paper, we propose a QoS multicast routing algorithm based on artificial fish swarm optimization. Meeting with the Bandwidth-delay-constrained, the proposed algorithm can search the least-cost multicast routing tree quickly. Simulation results show that this algorithm has high reliability and good performance of global optimization, and suit for real-time, high-speed multimedia transmission network.

关键词： QoS multicast routing problem artificial fish-swarm algorithm Steiner tree problem

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Estimation of Three-Parameter Weibull Distribution Based on artificial fish-swarm algorithm 18

Estimation of Three-Parameter Weibull Distribution Based on ...

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International Conference on Mathematics and Statistics (ICoMS)

作者： Zhang, Xiangpo Army Engn Univ PLA Field Engn Coll Nanjing Peoples R China

ISBN: (纸本)9781450365383

Three-parameter Weibull distribution (TPWD) plays an important role and is widely used in failure distribution modeling in reliability studies, which makes the estimation of its parameters very important and a hot study topic. In this paper, a new method of TPWD parameters estimation is proposed by integrating the artificial fish-swarm algorithm (AFSA) with the maximum likelihood estimation (MLE) method. In contrast to the existing methods, where the maximum log-likelihood value is obtained by solving the maximum likelihood equations set, the log-likelihood maximization is achieved directly using AFSA in the proposed method. And then the parameters of TPWD can be obtained according to the maximum likelihood value. The case study shows that the new method proposed in this paper is easy to be processed and has a good precision. It provides a new and highly efficient way to estimate the parameters of TPWD, and therefore provides a new way to evaluate the reliability and life distribution of products whose life distributions are considered as typical TPWD.

关键词： Three-parameter Weibull distribution (TPWD) Parameters estimation Maximum likelihood estimation artificial fish-swarm algorithm Reliability

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Reliability analysis of slope stability at seismic action based on artificial fish-swarm algorithm

Reliability analysis of slope stability at seismic action ba...

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International Conference on Applied Mechanics and Mechanical Engineering

作者： Shi, Beixiao Liu, Yanmin Mu, Zhiyong Hebei Univ Engn Coll Resources Handan Peoples R China Hebei Univ Engn Coll Water Conservancy & Hydropower Handan Peoples R China

ISBN: (纸本)9780878492459

Based on transient analysis,stress value and values of principal stress of slope can be got at each time. Then based on partial failure discriminance, the only slip plane can be got to some initial point. Thirdly, based on sweden slice method, formula of the safety coefficient of the slope's stability can be as objective function, through artificial fish-swarm algorithm, slip plane can be established. Finally, through analyzing the safety coefficient of the slope's stability and initial point in each time, the structure reliability of slope stability and failure probability can be got. Finally, this method is verified by an example, which proved that the method is feasible and available.

关键词： seism structure reliability artificial fish-swarm algorithm safety coefficient

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artificial fish-swarm algorithm BASED PARAMETER LEARNING METHOD FOR BAYESIAN NETWORK

ARTIFICIAL FISH-SWARM ALGORITHM BASED PARAMETER LEARNING MET...

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2009中国宁波国际计算机科学与技术学术大会

作者： Yan Wang Changming Zhang Dewen Wang School of Computer Science and Technology North China Electric Power University

The problem of learning Bayesian network parameter is known to be a hard *** this paper,a parameter learning method based on artificial fish-swarm algorithm(AFSA) is presented for Bayesian networks with Noisy-Or and Noisy-And *** learning approach is expatiated and the convergence is improved by adjusting the random move's speed of artificial *** experimental results show that this learning method is feasible and preferable.

关键词： Bayesian Network Parameter Learning artificial fish-swarm algorithm Noisy-Or Noisy-And

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artificial fish-swarm algorithm BASED PARAMETER LEARNING METHOD FOR BAYESIAN NETWORK

ARTIFICIAL FISH-SWARM ALGORITHM BASED PARAMETER LEARNING MET...

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2009 International Symposium on Computer Science and Technology(2009 中国宁波国际计算机科学与技术学术大会)

作者： Yan Wang Changming Zhang Dewen Wang School of Computer Science and Technology North China Electric Power University Baoding 071000P.R.China

The problem of learning Bayesian network parameter is known to be a hard *** this paper, a parameter learning method based on artificial fish-swarm algorithm (AFSA) is presented for Bayesian networks with Noisy-Or and Noisy-And *** learning approach is expatiated and the convergence is improved by adjusting the random move's speed of artificial *** experimental results show that this learning method is feasible and preferable.

关键词： Bayesian Network Parameter Learning artificial fish-swarm algorithm Noisy-Or Noisy-And

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Prediction of Public Building Energy Consumption by Using artificial fish-swarm algorithm

Prediction of Public Building Energy Consumption by Using Ar...

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Chinese Automation Congress (CAC)

作者： Li Ming-hai Liu Min Xian Univ Architecture & Technol Sch Informat & Control Engn Xian Peoples R China

ISBN: (纸本)9781467371896

In order to overcome the shortage of traditional BP neural network method in the prediction of public building energy consumption, this paper proposes a neural network prediction model based on time series self-correlation analysis. Firstly, we determined the dimension of the input variables based on the energy consumption of building standards of self-correlation analysis, then combined with artificial fish-swarm algorithm, which has the advantage of higher optimization speed and easy to jump out of the extreme, to optimize initial weights and threshold value of the BP neural network, and established the energy consumption prediction model, finally, we used the model to predict a month's energy consumption values of a university building in Xi'an. The results show that compared with the traditional BP neural network model, this model has faster convergence speed and prediction accuracy is in plus or minus one, and the prediction error decreases with the increase of the number of iterations.

关键词： self-correlation analysis artificial fish-swarm algorithm BP neural network energy consumption prediction

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Random active shield generation based on modified artificial fish-swarm algorithm

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COMPUTERS & SECURITY 2020年 88卷 101552-000页

作者： Xin, Ruishan Yuan, Yidong He, Jiaji Zhen, Shuai Zhao, Yiqiang Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Tianjin Key Lab Imaging & Sensing Microelect Tech Tianjin 300072 Peoples R China

Active shield has already been a primitive sensor of security critical integrated circuits for detecting invasive attacks. Because of the complex topology structure, the active shield based on random Hamiltonian path has a high security level. However, the available generation algorithms of this random path have poor efficiency when shield area is large, restricting its application in integrated circuits. In this paper, a novel generation algorithm of random active shield is proposed using a modified artificial fish-swarm algorithm. By changing the random selection strategy of the generation process, the proposed algorithm makes each selection turn into a successful combination, thus improving the efficiency greatly. Simulations prove that this algorithm is seventeen times faster than the classical Cycle Merging algorithm, while keeping good randomness. Meanwhile, the proposed algorithm is capable of large shield generation. In a 0.18 mu m CMOS process with the minimum top-metal width and space of 1.5 mu m, the active shield with the area of 3 x 3 mm(2) only needs approximately 2 h for generation. (C) 2019 Published by Elsevier Ltd.

关键词： Integrated circuits Invasive attacks Active shield Random Hamiltonian path artificial fish-swarm algorithm

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A Novel Self-Adaptation Hybrid artificial fish-swarm algorithm

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IFAC Proceedings Volumes 2013年第5期46卷 583-588页

作者： X.T. Hu H.Q. Zhang Z.C. Li Y.A. Huang Z.P. Yin No.38 Research Institute of CETC Hefei 230088 China (Tel: 86-0551-5391771) State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan 430074 China

In this paper, the social behaviors of fish swarm were classified in four ways: foraging behavior, stray behavior, reproductive behavior, and escaping behavior. Inspired by this, a novel artificial fish swarm algorithm (NAFSA) was proposed, which integrated the merits of the self-adaptation strategy, mutation strategy and hybrid strategy into the social behaviors of fish swarm. In the case of mutation strategy, the cloud theory was introduced into the escaping behavior, and the basic cloud generator was used as the mutation operator because of the properties of randomness and stable tendency of a normal cloud model. For the hybrid strategy, the selection, crossover and mutation operator in evolutionary algorithm were applied to define the reproductive ability of an artificial fish. Furthermore, the parameters of Step and Visual were developed in forms of hyperbolic tangent function to adjust the optimize performance dynamically during iterations process. Finally, ten standard test functions are used as the benchmark to validate the effectiveness of the NAFSA. Experimental results confirmed the superiority of NAFSA in terms of both solution quality and convergence speed.

关键词： optimization algorithm artificial fish-swarm algorithm cloud model evolutionary algorithm

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Attribute reduction based on niche artificial fish-swarm algorithm

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ICIC Express Letters 2014年第12期8卷 3523-3528页

作者： Du, Xlaoxin Zhang, Jlanfei Wang, B. College of Computer and Control Engineering No. 42 Wenhua Street Qiqihar China College of Educational and Technology Qiqihar University No. 42 Wenhua Street Qiqihar China

Although artificial fish-swarm algorithm has good ability to obtain the global extremum and is not sensitive to initial value, it is easy to trap in local extremum and has slow convergence speed when artificial fish-swarm appears more serious aggregation in non-global extreme value point at the late search, which leads to the search performance degradation. In this paper, we introduce niche technology into artificial fish-swarm algorithm, and build niche artificial fish-swarm algorithm. The algorithm avoids problems ofgetting into local extremum and slow convergence speed, and keeps the population diversity, so the algorithm can search multiple optimal solutions in global scope. At last, we apply the algorithm to the problem ofattribute reduction, the theoretical analysis and experimental results show that the algorithm is feasible and can obtain good reduction results. © 2014.

关键词： artificial fish-swarm algorithm Attribute reduction Mutation strategy Niche

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A modified artificial fish-swarm algorithm

A modified artificial fish-swarm algorithm

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6th World Congress on Intelligent Control and Automation

作者： Xiao, Jianmei Zheng, Xiaoming Wang, Xihuai Huang, Youfang Shanghai Maritime Univ Coll Logist Engn Shanghai 200135 Peoples R China

ISBN: (纸本)1424403316

artificial fish-swarm algorithm (AFSA) is a novel optimizing method. It has a strong robustness and good global astringency, and it is also proved to be insensitive to initial values. However, it has some defects as low optimizing precision, and low speed of astringency in the later period of the optimization. In this paper, an adaptive AFSA algorithm is presented by adjusting the parameter automatically in basic AFSA. Six benchmark functions are used to check the performance of the new method. It shows the adaptive AFSA algorithm has great effect on improving the precision of optimization.

关键词： artificial fish-swarm algorithm optimization adaptive swarm intelligence

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