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Optimization of machining process route for internal joint parts using artificial fish swarm algorithm

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2025年第1期19卷

作者： Han, Jun Zhu, Junwei Zhang, Yang Zhao, Zhenyao An, Zexi Inner Mongolia Univ Sci Technol Coll Mech Engn 7 Alding Rd Baotou 014010 Inner Mongolia Peoples R China

This paper addresses the issue of low processing efficiency resulting from the intricate process of an internal joint component. To this end, it proposes an optimization of the processing route for the component through the implementation of an artificial fish swarm algorithm based on the existing process route. In accordance with the characteristics of the part, the processing element is meticulously delineated, a prudent processing element code is formulated, the adjacent processing elements of the same category are consolidated, and the process constraints of the part are scrutinized before and after the merger. A reasonable constraint matrix model is constructed. The objective function is to determine the minimum number of machine tool, cutting tool, and clamping type changes. The design of prey, swarm, and follow behavioral parameters is conducted in a reasonable manner, as is the optimization and adjustment of the algorithm. By comparing and verifying the pre-optimization and post-optimization process solutions, the study shows that the combined optimized solution reduces the total number of machine tool changes, tool changes, and clamping changes by 43.8% and reduces the total machining time by 7 minutes and 16 seconds, which is more efficient and reasonable.

关键词： Manufacturing feature Manufacturing step cell Constraint matrix Process optimization artificial fish swarm algorithm

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A physics-informed artificial fish swarm algorithm for multiple tunnel fire source locations prediction

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2024年 199卷

作者： Sun, Bin Guo, Tong Southeast Univ China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China

To address multiple tunnel fire source locations prediction for better firefighting, this work aims to establish a model-driven and data-driven fusion algorithm, which is named as the physics-informed artificial fish swarm algorithm. In the algorithm, the artificial fish swarm algorithm is utilized to avoid complex fire mechanisms modelling, which makes the algorithm owns the advantage of the data-driven methods. Meanwhile, a physical model of the longitudinal ceiling temperature distribution relative to fire source locations and some physical mechanisms are utilized to guide the data-driven artificial fish swarm algorithm for converging to the reasonable prediction, which makes the algorithm owns the advantage of the model-driven methods. Two full-scale tunnel fire experiments with two pool fire sources and three pool fire sources as well as one full-scale cable fire experiment in the tunnel are utilized to support and verify the algorithm, which can be utilized to achieve multiple tunnel fire sources prediction and ceiling temperature distribution prediction in tunnel fires.

关键词： Tunnel fire Multiple fire source locations prediction artificial fish swarm algorithm Ceiling temperature distribution Full scale tunnel fire experiments

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Mining high average utility itemsets using artificial fish swarm algorithm with computed multiple minimum average utility thresholds

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1597-1613页

作者： Nandhini, S. S. Kannimuthu, S. Bannari Amman Inst Technol Sathyamangalam Tamil Nadu India Karpagam Coll Engn Coimbatore Tamil Nadu India

It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using artificial fish swarm algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and userdefined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms.

关键词： artificial fish swarm algorithm data mining frequent itemset mining high average utility itemsets itemset mining utility mining

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Development of online sports guiding assurance system based on artificial fish swarm algorithm and intelligent speech semantic extraction

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第3期16卷 939-948页

作者： Kang, Dongning Shijiazhuang Univ Appl Technol Adm Off Shijiazhuang 050081 Hebei Peoples R China

Under the recent condition of information technology, intelligent sports teaching management needs to change the traditional mode to improve the efficiency. At present, modern multimedia technology has fully integrated network communication technology, and teaching work is more carried out in the network conditions, which makes the teaching mode completely changed. Because of characteristics of the autonomous and interactive, network teaching mode makes the communication between students, students and teachers easier and closer. The traditional single auxiliary teaching mode can not quickly feedback various information defects are also solved by the network teaching mode. The network teaching mode has own advantages. It can not only fully mobilize students' enthusiasm for learning, but also change the traditional teaching mode. In this paper, we propose the online sports guiding assurance system based on artificial intelligence algorithm and intelligent speech semantic extraction. The objectives of the study contains the following aspects: (1) AFSA algorithm has the advantages of good global convergence, low initial value requirements, strong robustness and so on. So we consider this as the model for the AI. (2) In B/S multi-layer structure, the hierarchy is divided not based on physical structure but according to its structural logic, so we consider this as the platform structure. The experimental results show that the proposed algorithm performs well in error correction, duplication processing and data integration in database text matching. The proposed model performs best on each test set, and its accuracy is generally higher than that of traditional algorithms such as SVM, CNN, KNN and FCM. On the test set, the accuracy of the proposed method is 0.9896, while the accuracy of SVM, CNN, KNN and FCM are 0.9175, 0.9235, 0.9389 and 0.9564 respectively.

关键词： artificial intelligence (AI) Genetic algorithm Semantic analysis Network teaching Physical guiding artificial fish swarm algorithm

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Condition assessment of a concrete filled steel tube arch bridge using in-situ vibration measurements and an Improved artificial fish swarm algorithm

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COMPUTERS & STRUCTURES 2024年 291卷

作者： Qin, Shiqiang Feng, Jiacheng Tang, Jian Huo, Xuejin Zhou, Yunlai Yang, Fei Wahab, Magd Abdel Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Hubei Peoples R China China Railway Major Bridge Reconnaissance & Design Wuhan 430074 Hubei Peoples R China Xi An Jiao Tong Univ Sch Aerosp Engn State Key Lab Strength & Vibrat Mech Struct Xian 710049 Peoples R China Infrastructure Inspection Res Inst China Acad Railway Sci Corp Ltd Beijing 100081 Peoples R China Univ Ghent Fac Engn & Architecture Soete Lab Technologiepark Zwijnaarde 903 B-9052 Zwijnaarde Belgium Yuan Ze Univ Coll Engn Taoyuan City Taiwan

This study aims to assess the condition of a Concrete-Filled Steel Tube (CFST) arch bridge using in-situ vibration measurements, Finite Element Model Updating (FEMU) and an Improved artificial fish swarm algorithm (IAFSA). Dynamic coefficients, derailment coefficients and wheel unloading rates are extracted from the responses of running safety assessment. An updated finite element model using a kriging model and IAFSA is employed to evaluate the strength and rigidity of the structures. The results indicate that the natural frequencies and damping ratios of the CFST arch bridge meet the requirements of design code. The maximum bridge dynamic coefficient is found to be 1.085, indicating that the bridge dynamic performance is good. The wheel unloading rates of vehicle and locomotive exceed the specification limit, indicating that the track condition has to be improved before the bridge is operated. By updating an initial FE model, the errors between the predicted and the measured parameters are reduced to less than 5%.

关键词： Continuous rigid frame Concrete filled steel tube Arch bridge Finite Element Model Updating artificial fish swarm algorithm

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Multi-unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2024年第4期72卷

作者： Ding, Tao Zhong, Wenhan Cai, Yufeng China Jiliang Univ Hangzhou Peoples R China Zhejiang Light Ind Prod Inspect & Res Inst Hangzhou Peoples R China

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

关键词： odor source location artificial fish swarm algorithm unmanned aerial vehicle active olfaction

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Generation of unidirectional fiber random distribution structures based on an artificial fish swarm algorithm with random deletion after fiber filling

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JOURNAL OF COMPOSITE MATERIALS 2023年第15期57卷 2353-2365页

作者： Liu, Jinsong Li, Yibo Huang, Minghui Zeng, Lang Lu, Yan Cent South Univ Light Alloy Res Inst Changsha Peoples R China Cent South Univ Sch Mech & Elect Engn Changsha Peoples R China Cent South Univ Light Alloy Res Inst 932 South Lushan Rd Yuelu St Changsha 410083 Hunan Peoples R China

This study proposes a random distribution method for generating high-content volume fractions of fibers in their cross-sectional area. This approach is referred to as the artificial fish swarm algorithm (AFSA) with random deletion after fiber filling (RDAFF_AFSA). Initial fibers were first generated using AFSA, followed by fiber filling the matrix-rich region with a hard-core model. The desired representative volume element (RVE) was ultimately obtained by random deletion. The proposed method can generate RVEs with high-content volume fractions (up to 67%) of fibers. The generated RVEs were statistically analyzed and compared with the completely spatial random (CSR) pattern and experiment data. The results showed that RDAFF_AFSA exhibited a high degree of consistency with the CSR and experimental data. The elastic constants of the carbon fiber-reinforced plastic composites were predicted by finite element analysis. The predicted results are very reasonable compared to the experimental results. The proposed method can provide a highly valuable alternative for micromechanical and multiscale analyses of unidirectional fiber-reinforced composites.

关键词： artificial fish swarm algorithm representative volume element generation fiber random distribution finite element analysis

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Identification of thermal conductivity of transient heat transfer systems based on an improved artificial fish swarm algorithm

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第14期148卷 6969-6987页

作者： Wang, Shoubin Li, Yunlong Zhou, Yuan Peng, Guili Xu, Wenbin Tianjin ChengJian Univ Sch Control & Mech Tianjin 300384 Peoples R China Harbin Inst Technol Sch Instrument Sci & Engn Harbin 150001 Peoples R China

Thermal conductivity is employed as a measure of the strength of heat transfer from a substance. There is a lack of accurate methods for solving thermal conductivity in heat transfer inverse problems due to the ill-posedness and non-linearity of the problem. In this paper, an intelligent solution algorithm based on the artificial fish swarm algorithm is proposed and a proportional control algorithm is introduced to invert the heat transfer coefficient of a transient heat conduction system. In the forward heat conduction process, a finite difference algorithm is utilized to solve for the required measurement point temperature of the heat conduction system. In the inverse problem solution, the performance of the algorithm is compared before and after its improvement, and the inversion is compared in the rectangular and circular heat transfer domains under the influence of different heat transfer materials, interference sources and errors. The results show that in the absence of disturbances, the inversion errors of the improved artificial fish swarm algorithm in the rectangular and circular domains are 0.4% and 0.1%, respectively. In the presence of large measurement errors (sigma = 0.5 ), the inversion errors in the rectangular and circular domains are 8.7% and 2.4%, respectively, which validate the accuracy of the algorithm.

关键词： Heat transfer inverse problem artificial fish swarm algorithm Proportional regulation Thermal conductivity Finite difference method

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Path planning for autonomous surface vessels based on improved artificial fish swarm algorithm: a further study

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SHIPS AND OFFSHORE STRUCTURES 2023年第9期18卷 1325-1337页

作者： Zhao, Liang Bai, Yong Wang, Fang Bai, Jie Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou Peoples R China Harbin Engn Univ Sci & Technol Underwater Vehicle Technol Lab Harbin Peoples R China

Featuring agile controllability, high-level autonomy, and powerful field operational advantages, autonomous surface vessels (ASVs) are gaining increasing attention worldwide. Central to the control of ASVs, path planning is one of the key technologies that ensure the navigation safety. However, many of the previous works to solve the path planning problem are to find a collision-free and shortest path, but the solutions are not satisfied by the safety requirements and the non-holonomic constraints of ASVs. This paper expands on our previous work on the improved artificial fish algorithm (IAFSA) to address these challenges. The expanding technique is introduced to modify the grid cost, and a B-spline-based path smoother is applied to the algorithm to enhance its feasibility in cooperating with the ASV controller. Simulation experiments have been conducted to illustrate the effectiveness of the proposed method. Moreover, the new algorithm is tested in the USV control system model in a practical environment. The simulation results have demonstrated that the proposed method is more efficient than the other state-of-the-art algorithms, and the simulation with the ASV model illustrated its excellent performance cooperating with the control system. Therefore, the proposed method can be considered as a reliable algorithm to solve the path planning problem of ASVs.

关键词： Path planning artificial fish swarm algorithm unmanned surface vehicle Optimization Collision avoidance USV

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An adaptive stochastic resonance detection method with a knowledge-based improved artificial fish swarm algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第8期81卷 11773-11794页

作者： Hao, Jing Huang, Fuyu Shen, Xuejv Jiang, Chundong Lin, Xiaoran Hebei Univ Econ & Business Sch Informat Technol Shijiazhuang Hebei Peoples R China Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Hebei Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

The selection of the parameters plays a decisive role in the detection performance of parameter- tuning stochastic resonance (SR) system. In order to solve the problems that the existing adaptive stochastic resonance (ASR) system is easy to fall into local optimum and slow convergence rate in parameter optimization, an ASR detection method with knowledge-based improved artificial fish swarm algorithm (AFSA) is proposed. The lens based Opposition-based Learning strategy (lensOBL) is introduced into the AFSA, micro and macro adjustments can be made to enhance the global search ability, especially in the later stage of the algorithm. In addition, in order to speed up the convergence rate of the optimization, the relationship between the system structure parameters and the SR phenomenon produced is considered to guide the optimization process of the algorithm. Compared with the ASR methods based on the AFSA, the proposed method can obtain better parameters and improves the detection performance of the system. Additionally, to solve the problem of detecting multifrequency weak signals submerged in strong noise and many frequency bandwidths, firstly, the SR of the weak signal of large parameters is realized through adjusting the damping coefficient and the system shape parameters, which solves the problem that the traditional SR is only suitable for small parameter weak signal detection. Then, parallel ASR detection systems are designed based on the idea of the proposed method to identify multifrequency weak radio signals. Finally, simulation data and experimental analysis show that, compared with other adaptive parameter-tuning SR methods, the proposed method enhances the convergence speed and accuracy of optimization, has good robustness, and improves the diversity of fish swarm. It enables the optimal detection of multifrequency weak signal better in strong noise background, and broadens the potential applications of weak signal detection methods based on the principle

关键词： Adaptive stochastic resonance Multifrequency weak signal artificial fish swarm algorithm Parameter optimization

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