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An Improved artificial fish swarm algorithm and Its Application in Multiple Sequence Alignment

JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2014年第3期11卷 888-892页

作者： Yang, Wei-Hong Jilin Business & Technol Coll Dept Comp Sci Changchun 130062 Peoples R China

In this paper, a hybrid optimization algorithm for the Multiple Sequence Alignment is presented. Our method is presented artificial fish swarm algorithm by employing some improvements. The goal of our method is to reduce the required computing time and get better solution. In our simulations we use the instances of TSPLIB to show that the speed of convergence of the improved algorithm can be enhanced greatly compared with the traditional algorithm, we also present the comparison of the improved algorithm and the traditional algorithm on different sequences.

关键词： artificial fish swarm algorithm swarm Intelligence Multiple Sequence Alignment

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artificial fish swarm Optimization with Deep Learning Enabled Opinion Mining Approach

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Computer Systems Science & Engineering 2023年第4期45卷 737-751页

作者： Saud S.Alotaibi Eatedal Alabdulkreem Sami Althahabi Manar Ahmed Hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social ***,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process *** OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into ***,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification ***,in this paper,a new artificial fish swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM *** major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual *** addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction ***,BLSTM model is employed for the effectual detection and classification of ***,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

关键词： Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning

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Short-term Traffic Flow Forecasting based on SVR with Improved artificial fish swarm algorithm

Short-term Traffic Flow Forecasting based on SVR with Improv...

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第25届中国过程控制会议

作者： Wei-Hong YAO Ren-Xiao FANG Xiao-Yuan HUANG College of Computer Science and Technology Dalian University of Technology

Real-time and accurate traffic flow forecasting is one of the key contents of Intelligent Transportation *** recent years,the Support Vector Regression(SVR)has been widely used to solve nonlinear regression and time series *** improved artificial fish swarm algorithm along with the adaptive search mechanism is applied to optimize SVR due to the disadvantage of parameter selection of SVR in this *** a short-term traffic flow forecasting model based on IAFS-SVR is *** results show that the proposed method has better prediction performance than other exiting models,and is a promising alternative for short-term traffic flow forecasting.

关键词： short-term traffic flow forecasting artificial fish swarm algorithm support vector regression adaptive search mechanism

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Automated test case generation using an artificial fish swarm algorithm

Automated test case generation using an artificial fish swar...

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2014 International Conference on artificial Intelligence and Industrial Application (AIIA2014)

作者： C.Zeng Z.C.He M.Xie Institute of Electronic Engineering China Academy of Engineering Physics

ISBN: (纸本)9781784660529

Automated test case generation takes an important role in the software testing area, for which, an approach based on the artificial fish swarm algorithm is proposed. The experimental results verify the validity and feasibility of the proposed approach, and the results show that the approach is better than a genetic algorithm(GA), the PSO algorithm and the immune genetic algorithm.

关键词： artificial fish swarm algorithm software testing test cases

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Coverage Optimization Scheme Based on artificial fish swarm algorithm for Wireless Sensor Networks in Complicated Environment

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2014年第5期7卷 105-118页

作者： Hong, Lei Zhong, Rui Jingling Inst Technol Coll Informat Technol Nanjing 211169 Jiangsu Peoples R China

With regard to the subject of sensor network node optimization, a strategy for wireless sensor network coverage optimization based on improved fish swarm algorithm is proposed in this paper. The improved algorithm is targeted on network coverage, node utilization rate and energy consumption balance, which makes use of the ergodic property of chaotic motion to overcome the disadvantage that artificial fish swarm algorithm may easily lead to regional optimization. As for this, the global searching ability of algorithm is improved and the solving efficiency is optimized. Moreover, the algorithm is able to adapt to complicated environment. Shown by related simulation experiment, the improved fish swarm algorithm could effectively optimize sensor network node deployment and improve network coverage rate. Compared with basic artificial fish swarm algorithm, improved fish swarm algorithm increases network coverage rate by 8.9%.

关键词： Wireless sensor networks coverage optimal artificial fish swarm algorithm

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Application of an artificial fish swarm algorithm in Symbolic Regression

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第4期E96D卷 872-885页

作者： Liu, Qing Odaka, Tomohiro Kuroiwa, Jousuke Ogura, Hisakazu Univ Fukui Grad Sch Engn Dept Nucl Power & Energy Safety Engn Fukui 9108501 Japan

An artificial fish swarm algorithm for solving symbolic regression problems is introduced in this paper. In the proposed AFSA, AF individuals represent candidate solutions, which are represented by the gene expression scheme in GEP. For evaluating AF individuals, a penalty-based fitness function, in which the node number of the parse tree is considered to be a constraint, was designed in order to obtain a solution expression that not only fits,the given data well but is also compact. A number of important conceptions are defined, including distance, partners, congestion degree, and feature code. Based on the above concepts, we designed four behaviors, namely, randomly moving behavior, preying behavior, following behavior, and avoiding behavior, and present their respective formalized descriptions. The exhaustive simulation results demonstrate that the proposed algorithm can not only obtain a high-quality solution expression but also provides remarkable robustness and quick convergence.

关键词： artificial fish swarm algorithm symbolic regression parse tree optimization penalty

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Diagnostic Complexity of Regional Groundwater Resources System Based on time series fractal dimension and artificial fish swarm algorithm

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WATER RESOURCES MANAGEMENT 2013年第7期27卷 1897-1911页

作者： Yu, Miao Liu, Dong Bazimenyera, Jean de Dieu Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Water Saving Agr Univ Heilongjiang Prov Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Minist Agr Key Lab High Efficient Utilizat Agr Water Resourc Harbin 150030 Heilongjiang Peoples R China

Due to the increasing high rate of economic development and population, groundwater resources of Jiansanjiang in China are affected by both anthropogenic and natural factors. In order to achieve optimal local allocation of water resources and promotion of local economic development, a suitable method for measuring the complexity of groundwater resources system is very important. In this paper, time series fractal dimension based on the curve length calculation combined with the artificial fish algorithm for the intelligent efficient fitting of data were applied to diagnose groundwater sequence in Jiansanjiang. Fractal dimension values of monthly groundwater depth series in 15 districts of Jiansanjiang Branch Bureau and their average complexity were calculated and the results revealed that the complexity of north district is the highest while that of southern district is the lowest. Our analysis also revealed that the most important influencing factor of local groundwater depth dynamics variation is the human activities and results confirmed that combined fractal theory and artificial fish algorithm for extraction hydrological time series complexity feature is feasible and can be applied in studying regional hydrological process. It also provided a scientific basis for achieving sustainable utilization of the regional groundwater resource.

关键词： Time series fractal dimension artificial fish swarm algorithm Ground water depth Complexity Jiansanjiang Branch Bureau

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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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Improved AFSA-Based Energy-Aware Content Caching Strategy for UAV-Assisted VEC

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2025年第2期10卷 366-377页

作者： Long, Kejun Li, Chunlin Jiang, Kun Wan, Shaohua Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan 430063 Hubei Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Guangdong Peoples R China

UAV-assisted VEC can provide content caching services for vehicles by flying close to the vehicles for vehicle's QoS. However, in real-world scenarios with traffic congestion, due to the battery capacity and cache space limitations of UAVs, low content response speed and high response latency may occur. Based on this, we proposed a dynamic energy consumption-based content caching strategy in UAV-assisted VEC. We use the PSO algorithm to solve the problem and obtain the optimal UAV deployment location. For content caching, we construct a content caching model by considering UAV deployment, vehicle user preference, UAV cache capacity, and UAV energy consumption with the goal of minimizing content request latency. In addition, we propose an IAFSA-based content caching strategy. We reduce the solution space of the fish swarm algorithm, decrease the number of caching decisions, and improve the convergence performance of AFSA by employing dynamic horizons and step sizes. Experimental results show that the proposed IAFSA effectively reduces the average content request latency of the vehicle, improves the cache hit rate, and reduces the number of content return trips. Particularly, the proposed strategy reduces the average content request latency by more than 9.84% compared to the baseline algorithm.

关键词： Autonomous aerial vehicles Energy consumption Data centers Trajectory Wireless communication Vehicle dynamics Heuristic algorithms artificial fish swarm algorithm content caching energy efficiency vehicular edge computing

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Improvement for an optimization algorithm and its application in structural model updating

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2025年 1-11页

作者： An, Yonghui Zhong, Yue Yang, Jia-Hua Duan, Yuanfeng Ou, Jinping Dalian Univ Technol Dept Civil Engn State Key Lab Coastal & Offshore Engn Dalian 116023 Peoples R China Guangxi Univ State Key Lab Featured Met Mat & Life cycle Safety Provincially & Ministerially Co Constructed Nanning 530004 Peoples R China Curtin Univ Ctr Infrastructural Monitoring & Protect Sch Civil & Mech Engn Bentley WA 6102 Australia Zhejiang Univ Dept Civil Engn Hangzhou 310058 Peoples R China

Model updating is one of the important components of structural health monitoring. artificial fish swarm algorithm (AFSA) is an effective optimization algorithm and can be used in model updating and many research fields. However, its efficiency still requires improvement. A series of improvements for the AFSA is proposed, i.e., accelerating the preying behavior, and setting swimming direction in the previous iteration as the prior direction in the next iteration. After an iteration, the direction of the maximum concentration point within visual distance is selected to swim towards and this direction information is saved;in the preying behavior of the next iteration, the saved direction is considered as the prior direction to prey;if the food concentration in the saved direction is higher than current position, the fish will swim a step in this direction;otherwise, it will prey randomly again. The De Jong Function is used to validate the proposed method and results show that the improvement enhances convergence performance and higher efficiency. Moreover, its application in the finite element model updating of a long-span prestressed concrete continuous rigid frame bridge is investigated;seventeen parameters of the bridge are selected to update its numerical model, and results show that the proposed improvement is successfully applied to model updating. Compared with the AFSA without the proposed improvement, it is more efficient and it saves 25.314% and 32.742% in computational time for the present two examples, respectively. The proposed improvement can be used in various optimization problems of different fields.

关键词： artificial fish swarm algorithm Model updating Rigid frame bridge Structural health monitoring Optimization

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