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Modelling of advanced persistent threat attack monitoring based on the artificial fish swarm algorithm

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第4期20卷 550-557页

作者： Zhang, Biaohan Sanming Univ Fujian Key Lab Agr IOT Applicat Sch Informat Engn Sanming Fujian Peoples R China

In recent years, advanced persistent threat (APT) has become one of the important factors that threaten the network security. Aiming at the APT attack defence problem, this paper proposes an APT attack monitoring method based on the principle of artificial fish swarm algorithm. The attack monitoring model is established by imitating the behaviour of the artificial fish swarm. The model was used to dynamically monitor the environment, and the APT attack index was simulated with the food consistence to monitor the position of the highest APT attack index. The experimental results show that the monitoring model designed by this method can not only effectively monitor and forecast the attack target, but also has good expansibility and practicability.

关键词： artificial fish swarm algorithm advanced persistent threat attack monitoring model

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Energy consumption parameter detection of green energy saving building based on artificial fish swarm algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第5-6期44卷 498-510页

作者： Yin, Lijun Yin, Haoran Xinxiang Vocat & Tech Coll Xinxiang 453006 Henan Peoples R China Yurun Holdings Grp Nanjing 210019 Jiangsu Peoples R China

In order to overcome the low-detection accuracy of traditional methods, an artificial fish swarm algorithm was proposed to detect the energy consumption parameters of green and energy-saving buildings. The type of energy consumption equipment in green and energy-saving buildings is analysed, and the electricity consumption of building energy consumption equipment is taken as the building energy consumption parameter. The hierarchical clustering method was used to establish the classification model of energy consumption parameters, and the energy consumption parameters were classified and processed, and the energy consumption parameters detection model was built, and the preliminary detection results of energy consumption parameters were obtained. The artificial fish swarm algorithm was used to construct the optimisation function of building parameter detection results to obtain the optimal detection results of energy consumption parameters. Experimental results show that the accuracy of the proposed method is between 92.76% and 98.75%, and the practical application effect is good.

关键词： artificial fish swarm algorithm green energy saving building energy consumption parameters hierarchical clustering

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Optimal Power Flow Based on Novel Multi-objective artificial fish swarm algorithm

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ENGINEERING LETTERS 2020年第2期28卷 542-550页

作者： Guo, Gang Qian, Jie Li, Shuaiyong Luoyang Normal Univ Sch Informat Technol Luoyang 471934 Henan Peoples R China Chongqing Univ Posts & Telecommun Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China

Computer technology provides new possibilities for handling the many-objective optimal power flow (MOOPF) problems with high-dimension and non-differentiability. As one of typical intelligent algorithms, the novel multi-objective artificial fish swarm algorithm (NMAFSA) is proposed to solve the MOOPF problems and realize the economical operation of power systems. The NMAFSA algorithm, which combines with optimal solution guidance (OSG) principle and non-inferior retention (NIR) mechanism, is effective to reduce the fuel cost, emission and power loss. Compared with the representative many-objective particle swarm optimization (MPSO) and non-dominated sorting genetic algorithm-II (NSGA-II), the superiority and adaptability of presented NMAFSA algorithm are validated. Six simulation trials are carried out on MATLAB software, including the dual-objective and triple-objective optimizations on three different scale power systems. Detailed results demonstrate that the suggested NMAFSA algorithm with stable-operation and fast-convergence has great potential to deal with the MOOPF problems more efficiently. Furthermore, the generation distance (GD) index also quantitatively proves that the NMAFSA algorithm can obtain the well-distributed Pareto front (PF).

关键词： artificial fish swarm algorithm Optimal power flow Computer technology Generation distance

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An artificial fish swarm algorithm for a multi-objective grain transportation problem

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EVOLUTIONARY INTELLIGENCE 2020年第1期13卷 9-19页

作者： Jia, Shiyu Zhou, Kang Zhou, Sisi Fang, Haocheng Zhen, Yiting Zou, Yilin Wuhan Polytech Univ Sch Math & Comp Wuhan 430023 Hubei Peoples R China

The problem of grain transportation optimization is a typical NP-complete problem. To solve the problem, it is necessary to construct a mathematical model for the optimization of grain transportation. As the single-objective grain transportation route optimization model is difficult to better simulate the complex and varied conditions in real life, the multi-objective grain transportation route optimization model is closer to reality and has more guiding significance for practical problems. Therefore, this paper constructs a multi-objective grain transportation optimization problem model. And improved the artificial fish swarm algorithm to make it can be better solution. First, a similar fragment distance is introduced to replace the traditional distance calculation method. Second, we play the guiding role of bulletin board to insert the optimal solution fragment in the bulletin board into the current solution. Finally, according to the characteristics of food transportation problems, three behaviors of artificial fish were improved and mixed neighborhood search was conducted. In simulation experiments, the precision of the traditional artificial fish algorithm and improved algorithm is more and more low with the increase of amount of data. The difference between that and the optimal solution in the database is becoming more and more big, but the error in not only path length but also the number of vehicles of the improved algorithm is still within the scope of the permit. The error of the traditional artificial fish algorithm is far beyond permissible range. Experimental results show that the improved artificial fish swarm algorithm achieves high solution accuracy in path length and the number of vehicles. However, because there is no time window constraint, the conflict between the number of vehicle and the path length is very small. Finally, the set of Pareto solutions converges to 1 or 2 points.

关键词： MOP GTOP artificial fish swarm algorithm Improved artificial fish swarm algorithm

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Development of an artificial fish swarm algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background

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Fluid Dynamics & Materials Processing 2020年第5期16卷 935-946页

作者： Sheng Bai Feng Bao Fengzhi Zhao Miaomiao Liu School of Computer and Information Technology Northeast Petroleum UniversityDaqing163318China

The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable *** artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic *** nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual *** results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level *** show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.

关键词： artificial fish swarm algorithm wireless sensor network network measurement hydrodynamics

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The artificial fish swarm algorithm Optimized by RNA Computing

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2021年第4期55卷 346-357页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Liang, Jingyi Tianjin Univ Commerce Informat Engn Coll Tianjin 300134 Peoples R China Tianjin Univ Commerce Econ Coll Tianjin 300134 Peoples R China

In the initial period, the peculiarity of artificial fish swarm algorithm is of fast searching speed and high optimization accuracy, but in the later period, the convergence speed is always slow, and artificial fish tend to gather around the local optimum. Therefore, the solving ability of the algorithm becomes weak and the global optimal value is hard to obtain. Considering the introduction of RNA computation based on biomolecular operations, the optimization capability of traditional algorithm can be enhanced effectively. Therefore, RNA computing is introduced to artificial fish swarm algorithm, and a modified artificial fish swarm algorithm is presented on the grounds of RNA computing. In the later period of artificial fish swarm algorithm, the transformation, replacement and recombination operations in RNA computation are applied to increase diversity of artificial fish, so as to further the convergence speed and optimization capability of the algorithm. In the meantime, the improved algorithm, RNA-AFSA, is tested by four typical functions, and the results prove that the modified artificial fish swarm algorithm has better optimization effects in search accuracy, stability, and other aspects.

关键词： artificial fish swarm algorithm RNA computation RNA-AFSA function optimization

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The artificial fish swarm algorithm Improved by Fireworks algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2022年第4期56卷 311-323页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Li, Hongbo Tianjin Univ Commerce Informat Engn Coll Tianjin 300134 Peoples R China Tianjin Univ Commerce Econ Coll Tianjin 300134 Peoples R China

The artificial fish swarm algorithm can achieve good convergence effects in the early stage, but in the late stage, the algorithm has the problems of slow convergence speed and low optimization accuracy, and it is easy to fall into local extremes, making the algorithm's convergence effect poor. Therefore, the characteristics of fireworks algorithm are used to improve the deficiency of fish swarm algorithm that is easy to fall into local extreme value in the late stage, and FWA-artificial fish swarm algorithm is put forward. When the effect of artificial fish swarm algorithm is poor, the explosion, mutation, mapping, and selection operations of fireworks algorithm are introduced to increase the variability of artificial fish, so as to enhance the optimization speed and ability of the algorithm. Finally, the improved algorithm is tested by four typical complex functions which are difficult to find the optimal solution by traditional method. Simulation results prove that the algorithm has the advantages of faster optimization speed, higher precision, and stronger stability.

关键词： artificial fish swarm algorithm fireworks algorithm FWA-artificial fish swarm algorithm function optimization

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Network Intrusion Detection Based on the Improved artificial fish swarm algorithm

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JOURNAL OF COMPUTERS 2013年第11期8卷 2990-2996页

作者： Wang, Guo Dai, Dong Henan Mech & Elect Engn Coll Xinxiang 453003 Henan Peoples R China

In order to predict network anomalies and get rid of the drawbacks of current detection, early prediction of abnormal for detecting early characteristics of the abnormal is introduced in the invasion anomaly detection process. First, the objective functions are constructed according to the feature subset dimensions and the detection accurate rates of the detection model. Then the artificial fish swarm algorithm is used to search the optimal feature subset and the chaotic, feedback mechanisms are introduced to improve the artificial fish swarm algorithm, the excessive intrusion feature rough sets produced in the classification process are simplified to guarantee the simplicity of characteristics and the estimation model for residuals gray level to predicate the early simplified invasion. Finally KDD1999 database is applied to testify the validity of the algorithm. The simulation results illustrate the improved artificial fish swarm algorithm can obtain the optimal intrusion feature subsets and reduce the dimensions of the feature subsets, which not only increase the network intrusion detection rates and reduce the errors, but also speed up the network abnormal intrusion detection.

关键词： artificial fish swarm algorithm feature selection chaotic mechanisms intrusion detection feedback mechanism

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Research on Port Logistics Distribution Route Planning Based on artificial fish swarm algorithm

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JOURNAL OF COASTAL RESEARCH 2020年第Spe115期36卷 78-80页

作者： Ouyang, Fei Henan Inst Econ & Trade Zhengzhou 450046 Peoples R China

As a sub-problem of the inventory routing problem, the transportation routing problem is one of the main problems to be solved in studying how to reduce the total cost of logistics. Through the establishment of a reasonable distribution path, it plays a very important role in the cost, speed and efficiency of the whole logistics transportation. Based on the concept of artificial fish swarm algorithm, combined with the characteristics of port logistics distribution process, this paper constructs an optimization model and uses artificial fish swarm algorithm to solve specific cases. Finally, Matlab 6.5 is used to program and the artificial fish swarm algorithm is verified, which proves the feasibility and effectiveness of the algorithm. The results show that the mathematical model and artificial fish swarm algorithm have certain theoretical guidance and reference value for solving the port logistics distribution path planning problem.

关键词： artificial fish swarm algorithm port logistics distribution path planning

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A Power Allocation algorithm Based on artificial fish swarm algorithm for Cognitive Radio Networks Based on Interference Alignment 4

A Power Allocation Algorithm Based on Artificial Fish Swarm ...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Guo, Kai Qiu, Runhe Cai, Caixia Donghua Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781538611074

In order to improve the overall quality of service (QoS) of cognitive radio network based on interference alignment (IA), an optimal power allocation algorithm based on artificial fish swarm algorithm is proposed in this paper. The algorithm uses artificial fish swarm algorithm to allocate the power of each user reasonably, so as to achieve the minimum average interruption probability of secondary user (SU). In order to ensure the communication quality of primary user (PU), this paper also proposes a minimum power threshold, the setting of this threshold ensures both the communication rate of PU and the outage probability of PU, and effectively guarantee the requirement for communication quality of PU. The simulation results also show that the proposed algorithm can effectively improve the overall communication quality of the system and minimize the average outage probability of the SU.

关键词： interference alignment cognitive wireless networks power allocation outage probability artificial fish swarm algorithm

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