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Action Recognition Method in Sports Video Shear Based on fish swarm algorithm

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2023年第4期19卷 554-562页

作者： Sun, Jie Lu, Lin China Univ Geosci Inst Phys Educ Wuhan Peoples R China

This research offers a sports video action recognition approach based on the fish swarm algorithm in light of the low accuracy of existing sports video action recognition methods. A modified fish swarm algorithm is proposed to construct invariant features and decrease the dimension of features. Based on this algorithm, local features and global features can be classified. The experimental findings on the typical sports action data set demonstrate that the key details of sports action can be successfully retained by the dimensionality-reduced fusion invariant characteristics. According to this research, the average recognition time of the proposed method for walking, running, squatting, sitting, and bending is less than 326 seconds, and the average recognition rate is higher than 94%. This proves that this method can significantly improve the performance and efficiency of online sports video motion recognition.

关键词： Action Recognition fish swarm algorithm Image Features Sports Video Sports Video Shear

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Modified normative fish swarm algorithm for optimizing power extraction in photovoltaic systems

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1135-1154页

作者： Tan, Weng-Hooi Mohamad-Saleh, Junita Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 11430 Penang Malaysia

Background: According to ASEAN Centre of Energy 2020 report, electrification rates in certain ASEAN countries are considerably low. The energy sector faces issues of unreliable supply, undersupply and high electricity rates. As power supply is an important source of daily requirement, this research work aims to improve the efficiency of solar power generation efficiency. In line with the aim, we proposed a modified Normative fish swarm algorithm (mNFSA) and applied it to Maximum Power Point Tracking (MPPT) process in Photovoltaic (PV) application systems. Significant modifications have been made to the original NFSA, including the removal of unnecessary features, the formulation of modified behaviors, the refinement of adaptive parameters as well as settings, and the implementation of the MPPT architecture. Results: A complete PV system model is constructed for simulation, in which 10 PV arrays with different shading levels are connected in series in the PV panel. For a consistent evaluation, 10 sets of data on maximum extracted power were collected during MPPT simulation process. The statistical data were recorded for comprehensive performance checks. Overall, mNFSA was compared with 6 other optimization algorithms, including 5 state-of-the-art algorithms and 1 related evolutionary algorithm. Conclusions: The results demonstrate that mNFSA outperforms other compared algorithms in terms of maximum extracted power and relative percentage error, demonstrating higher compatibility and effectiveness of mNFSA for MPPT. The statistical results support that mNFSA is one of the most robust algorithms and best suited for MPPT applications.

关键词： Global optimization fish swarm algorithm Maximum power point tracking Photovoltaic system Modeling

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Research on Transmission Control of Airborne Communication Data Link System Based on Artificial fish swarm algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2023年第4期57卷 327-336页

作者： Nie, Xiuzhen Jiao, Yingxue ShanXi Railway Vocat & Tech Coll Taiyuan Shanxi Peoples R China

To enhance the anti-interference performance of airborne communication data link system, a transmission control method based on improved fish swarm algorithm is constructed. Firstly, the airborne data link channel model is constructed. Secondly, equalization algorithm of airborne data link is constructed, and the improved artificial fish algorithm is designed, and the algorithm procedure of transmission control of airborne communication data link system is established. Finally, simulation analysis is carried out, results show that the proposed method has higher transmission success rate, lower transmission time, and higher transmission rate, the proposed model has better performance in transmission control of airborne communication data link system.

关键词： transmission control airborne communication data link system fish swarm algorithm channel

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SOLVING THE ECONOMIC LOT INSPECTION AND SCHEDULING PROBLEM USING THE ARTIFICIAL fish swarm algorithm AND FIXED RATE APPROACH

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Journal of Technology 2024年第3期39卷 157-168页

作者： Chang, Yu-Jen Chen, Shih-Chieh Department of Information Management Tunghai University Taichung407 Taiwan Department of Business Management Hsiuping University of Science and Technology Taichung412 Taiwan

This study solves the economic lot inspection and scheduling problem by using the fixed rate approach under the imperfect production system. The mathematical model uses the excess idle time of a production facility to reduce the production rate of a specific product in order to reduce the average total cost. This study uses the common cycle method and adopts a consensus inspection strategy to explore the optimal production cycle of this model and the optimal number of inspections for each product item, so as to minimize the total cost per unit time. At the same time, in order to consider the feasibility of production, the mathematical model has added three limiting formulas: production capacity, inspection capacity and inspection rate limit. In this study, the artificial fish swarm algorithm is used to solve the mathematical model of the above conditions, and a numerical experiment is used to illustrate the effectiveness of the proposed artificial fish swarm algorithm. The comparison with the results of applying the traditional approach and fixed rate approach shows that the fixed rate approach can effective reduce the total cost per unit time. © 2024, National Taiwan University of Science and Technology. All rights reserved.

关键词： economic lot fish swarm algorithm fixed rate inspection scheduling

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An Improved fish swarm algorithm for Neighborhood Rough Set Reduction and Its Application

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IEEE ACCESS 2019年 7卷 90277-90288页

作者： Zou, Li Li, Hongxin Jiang, Wei Yang, Xinhua Dalian Jiaotong Univ Software Technol Inst Dalian 116028 Peoples R China Dalian Jiaotong Univ Dalian Key Lab Welded Struct & Its Intelligent Mf Dalian 116028 Peoples R China Sichuan Prov Key Lab Proc Equipment & Control Zigong 643000 Peoples R China

In this paper, an improved fish swarm algorithm for neighborhood rough set reduction (IFSANRSR) is proposed. In IFSANRSR, by introducing an adaptive function to control the visual and step size of artificial fish, the problem of inconsistent convergence speed existed in a traditional artificial fish swarm algorithm (FSA) is avoided. The movement of artificial fish in the swarming and following behavior is improved to shorten the running time of the algorithm. The searching behavior is improved to enhance the local search ability without changing the global searching ability of the algorithm. By introducing the mechanism of extinction and rebirth, the worst solution is eliminated and rebirth takes place after each iteration, which ensures a high level of the overall fitness. The experimental results on three datasets from the University of California at Irvine (UCI) show that the attributes reduction by using the IFSANRSR has higher reduction rate and classification accuracy in most cases. It could better deal with real-valued data attributes and ensure the optimal attribute reduction set to be found. The experimental result on the decision system of aluminum alloy welded joints show that by using the IFSANRSR, the key influencing factors of fatigue life of aluminum alloy welded joints could be obtained, and the weight of each influencing factor could be calculated quantitatively.

关键词： fish swarm algorithm neighborhood rough set welded joints fatigue life influencing factor

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A fish swarm algorithm for Financial Risk Early Warning

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INTERNATIONAL JOURNAL OF ENTERPRISE INFORMATION SYSTEMS 2018年第4期14卷 54-63页

作者： Liu Yunshan Yunnan Commun Vocat & Tech Coll Sch Econ & Management Kunming Yunnan Peoples R China

The financial risk early warning system is important for promoting the sustainable development of enterprise, and in this article, the fish swarm algorithm is applied to it. First, the main financial risk factors of enterprise are summarized. Second, the index system of the financial risk early warning system is constructed based on relating theory. Third, the basic theory of artificial fish swarm algorithm is studied, and the mathematical models are constructed. Then, the wavelet neutral network is improved based on the fish swarm algorithm, and the algorithm procedure is designed. Finally, a simulation analysis is carried out, and the predicting correctness of samples is 100%, and results show that the fish swarm algorithm is an effective method for improving the financial risk early warning system.

关键词： Early Warning Financial Risk fish swarm algorithm Sustainable Development Of Enterprise

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Neighborhood rough set reduction with fish swarm algorithm

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SOFT COMPUTING 2017年第23期21卷 6907-6918页

作者： Chen, Yumin Zeng, Zhiqiang Lu, Junwen Xiamen Univ Technol Dept Comp Sci & Technol Xiamen 361024 Peoples R China Jiangxi Normal Univ State Int S&T Cooperat Base Networked Supporting Nanchang 330099 Jiangxi Peoples R China

Feature reduction refers to the problem of deleting those input features that are less predictive of a given outcome;a problem encountered in many areas such as pattern recognition, machine learning and data mining. In particular, it has been successfully applied in tasks that involve datasets containing huge numbers of features. Rough set theory has been used as such a data set preprocessor with much success, but current methods are inadequate at solving the problem of numerical feature reduction. As the classical rough set model can just be used to evaluate categorical features, we introduce a neighborhood rough set model to deal with numerical datasets by defining a neighborhood relation. However, this method is still not enough to find the optimal subsets regularly. In this paper, we propose a new feature reduction mechanism based on fish swarm algorithm (FSA) in an attempt to polish up this. The method is then applied to the problem of finding optimal feature subsets in the neighborhood rough set reduction process. We define three foraging behaviors of fish to find the optimal subsets and a fitness function to evaluate the best solutions. We construct the neighborhood feature reduction algorithm based on FSA and design some experiments comparing with a heuristic neighborhood feature reduction method. Experimental results show that the FSA-based neighborhood reduction method is suitable to deal with numerical data and more possibility to find an optimal reduct.

关键词： Granular computing Rough set theory Neighborhood system Feature reduction fish swarm algorithm

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An effective artificial fish swarm optimization algorithm for two-sided assembly line balancing problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 138卷 106121-000页

作者： Zhong, Yuguang Deng, Zexiao Xu, Ke Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Heilongjiang Peoples R China

Two-sided assembly lines are often used in assembly of large-sized products, such as automobiles, buses and trucks. Compared to the traditional one-sided assembly line, two-sided assembly line has advantages of shorter line and higher utilization of fixture. However, normal balancing method is not applicable to solve the two-sided assembly line balancing problem since the constraint conditions become more complicated. On the other hand, artificial fish swarm algorithm is a relatively new member of swarm intelligence based on swarm behaviors that were inspired from social behaviors of fish swarm in nature. As a typical application of behaviorism in artificial intelligence, artificial fish swarm algorithm can search for the global optimum. So it is a good candidate for developing new search algorithm for solving optimization problems in operational research. In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of matestation. Through extensive computational experiments, the performance of the proposed artificial fish swarm algorithm is examined. The experimental results validate the effectiveness and efficiency of the proposed method.

关键词： fish swarm algorithm Two-sided assembly line Assembly line balancing Optimization computing

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Finding rough set reducts with fish swarm algorithm

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KNOWLEDGE-BASED SYSTEMS 2015年 81卷 22-29页

作者： Chen, Yumin Zhu, Qingxin Xu, Huarong Xiamen Univ Technol Dept Comp Sci & Technol Xiamen 361024 Peoples R China Univ Elect Sci & Technol Dept Comp Sci & Technol Chengdu 611731 Peoples R China

Rough set theory is one of the effective methods to feature selection which can preserve the characteristics of the original features by deleting redundant information. The main idea of rough set approach to feature selection is to find a globally minimal reduct, the smallest set of features keeping important information of the original set of features. Rough set theory has been used as a dataset preprocessor with much success, but current approaches to feature selection are inadequate for finding a globally minimal reduct. In this paper, we propose a novel rough set based method to feature selection using fish swarm algorithm. The fish swarm algorithm is a new intelligent swarm modeling approach that consists primarily of searching, swarming, and following behaviors. It is attractive for feature selection since fish swarms can discover the best combination of features as they swim within the subset space. In our proposed algorithm, a minimal subset can be located and verified. To show the efficiency of our algorithm, we carry out numerical experiments based on some standard UCI datasets. The results demonstrate that our algorithm can provide an efficient tool for finding a minimal subset of the features without information loss. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Rough set theory Feature selection fish swarm algorithm swarm intelligence Reduction

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Robust Hybrid Artificial fish swarm Simulated Annealing Optimization algorithm for Secured Free Scale Networks against Malicious Attacks

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Computers, Materials & Continua 2021年第1期66卷 903-917页

作者： Ganeshan Keerthana Panneerselvam Anandan Nandhagopal Nachimuthu Anna University Chennai600025India Department of ECE C.Abdul Hakeem College of Engineering and TechnologyVelloreIndia Department of ECE Excel Engineering CollegeSalemIndia

Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node ***-free WSN is essential because they endure random attacks *** they are susceptible to malicious attacks,which mainly targets particular significant ***,the robustness of the network becomes important for ensuring the network *** paper presents a Robust Hybrid Artificial fish swarm Simulated Annealing Optimization(RHAFS-SA)*** is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree *** proposed RHAFS-SA is an enhanced version of the Improved Artificial fish swarm algorithm(IAFSA)by the simulated annealing(SA)*** proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence *** experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world *** experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.

关键词： Free scale networks robustness malicious attacks fish swarm algorithm

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