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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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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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artificial fish swarm algorithm Based Pilot Allocation in Massive MIMO System 16

Artificial Fish Swarm Algorithm Based Pilot Allocation in Ma...

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16th International Symposium on Communications and Information Technologies (ISCIT)

作者： Zhang, B. Bai, Z. Q. Li, J. H. Su, Y. Y. Sun, S. Q. Han, T. Kwak, K. S. Sch Informat Sci & Engn Jinan Peoples R China Shandong Univ Sch Phys Jinan Shandong Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Inha Univ Grad Sch Informat Technol & Telecommun Incheon South Korea

ISBN: (纸本)9781509040995

In order to solve the pilot pollution problem and realize the low complexity pilot allocation in 5G massive multiple-input and multiple-output (MIMO) communication system, we propose an artificial fish swarm algorithm based pilot allocation scheme in this paper. The algorithm is an imitation of the natural fish activities. Through the search of the region related to the largest user sum rate, we can find the optimal pilot distribution scheme. The pilot pollution model and muti-cell massive MIMO system model are presented. The artificial fish swarm algorithm is employed to find the optimal solution for the pilot allocation. Numerical results show the efficiency and advantages of the proposed algorithm compared with the conventional algorithm, such as Greedy algorithm and Tabu algorithm.

关键词： massive MIMO pilot allocation artificial fish swarm algorithm user rate

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artificial fish swarm algorithm for Job Shop Scheduling Problem 3

Artificial Fish Swarm Algorithm for Job Shop Scheduling Prob...

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3rd International Conference on Information and Communication Technology ICoICT

作者： Pythaloka, Dyah Wibowo, Agung Toto Sulistiyo, Mahmud Dwi Telkom Univ Sch Comp Bandung Indonesia

ISBN: (纸本)9781479977529

One of the most difficult combinatorial optimization problems in recent studies is job shop scheduling. Job shop scheduling which also holds the key to the company's profitability is a crucial problem faced by many manufacturing companies. Well-structured scheduling has the potential to reduce operating costs and increase profits. artificial fish swarm algorithm (AFSA) is one of optimization algorithms to solve combinatorial problems. This paper talks about the implementation of AFSA in job shop scheduling cases to produce an optimal solution, containing a minimum completion total time (makespan) of the entire job. The results showed that the AFSA which is designed for job shop scheduling problem optimization is able to provide solutions with the best efficiency value ever achieved was 75%. This figure is still considered unsatisfactory based on the makespan resulted. Nevertheless, the AFSA ability in the searching process for solutions is quite good considering that level of efficiency is achieved by only 10000 artificial fishes around 100 generations within 3,72e+41 solution spaces.

关键词： job shop scheduling optimization makespan artificial fish swarm algorithm

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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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Inversion of particle size distribution using the artificial fish swarm algorithm

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TM-TECHNISCHES MESSEN 2019年第1期86卷 34-44页

作者： Shan, Liang Xu, Liang Cao, Lixia Hong, Bo Wang, Daodang Guo, Tiantai Kong, Ming China Jiliang Univ Coll Metrol Technol & Engn Hangzhou 310018 Zhejiang Peoples R China China Jiliang Univ Coll Informat Engn Key Lab Electromagnet Wave Informat Technol & Met Hangzhou 310018 Zhejiang Peoples R China

In the small-angle forward scattering technique, an inversion method of particle size distribution (PSD) using artificial fish swarm algorithm (AFSA) is proposed, which can acquire optimal characteristic parameters of the PSD. Simulations are performed to verify the effectiveness of AFSA, in which spheroidal particles of a unimodal distribution that conforms to Johnson's S-B function is inversed at different levels of gaussian white noise. The comparison of the smooth objective function and non-smooth objective function of AFSA is conducted and discussed. The measurement system of the small-angle forward scattering based on Mie scattering theory is constructed and experiments are performed to verify the practicability of AFSA. A CCD sensor is adopted to receive the scattered light instead of the conventional photodetector. Pinhole is first proposed to replace standard particles and calibrate the measurement system, which can eliminate the influence of particle concentration. After the calibration experiments, the PSD of standardized polystyrene microspheres are measured and modified. Both the simulation and experiment results indicate that the PSD can be successfully inverted by AFSA with high reliability and stability in certain conditions, and the system calibration can further improve the inversion accuracy.

关键词： artificial fish swarm algorithm particle size distribution Mie scattering theory pinhole calibration

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Multiobjective artificial fish swarm algorithm for multiple sequence alignment

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INFOR 2020年第1期58卷 38-59页

作者： Dabba, Ali Tari, Abdelkamel Zouache, Djaafar Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Mohamed Elbachir Elibrahimi Univ Comp Sci Dept Bordj Bou Arreridj Algeria

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with singleobjective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

关键词： Multiple sequence alignment artificial fish swarm algorithm bioinspired algorithms bioinformatics optimization algorithms evolutionary algorithm molecular biology swarm intelligence

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Mechanism isomorphism identification based on artificial fish swarm algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第21期235卷 5421-5433页

作者： Yi, Huijun Wang, Jianpei Hu, Yongle Yang, Ping Univ Sci & Technol Hunan Prov Key Lab Intelligent Mfg Technol High p Changsha Peoples R China Nanjing Polytech Inst Nanjing Peoples R China Jiangsu Univ Sch Mech Engn Zhenjiang Jiangsu Peoples R China

The aim of this paper is to propose a practical solution for mechanism kinematic chain isomorphism identification - an artificial fish swarm algorithm. The artificial fish model of mechanism isomorphism identification is established, and behavioral way of the artificial fish is designed. According to isomorphism identification features of topological graph, the process of mechanism isomorphism identification based on artificial fish swarm algorithm is confirmed. The rationality and reliability of artificial fish swarm algorithm on the isomorphic identification of mechanism have been illustrated by a specific example, which provides a new method for intelligent CAD system design of mechanism. It builds a basis for future work in isomorphism identification of mechanism with high efficiency. Isomorphic identification of mechanism will contribute to rational qualitative analysis of mechanism design, perfection of irrationality can be done timely, which is the key factor for mechanical manufacturing. In this paper, we introduce the mechanism kinematic chain firstly, then optimization of artificial fish swarm algorithm is illustrated, and it is shown that how fish swarm algorithm is applied to mechanism kinematic chain. Finally, the feasibility and efficiency of the method are verified by the example of 10 bars, and the complex mechanism can be identified by the example of 14 bars and 18 bars.

关键词： Mechanism kinematic chain isomorphism identification artificial fish swarm algorithm simulation intelligent CAD

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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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Optimal chiller loading by improved artificial fish swarm algorithm for energy saving

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MATHEMATICS AND COMPUTERS IN SIMULATION 2019年 155卷 227-243页

作者： Zheng, Zhi-Xin Li, Jun-qing Duan, Pei-yong Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Southeast Univ Minist Educ China Key Lab Comp Network & Informat Integrat Nanjing 211189 Jiangsu Peoples R China

This study presents an improved artificial fish swarm algorithm (VAFSA) to solve the optimal chiller loading (OCL) problem, using minimal power consumption of chillers and cooling towers as the objective function. In the proposed algorithm, several components are developed, such as initialization method based decimal system, food concentration function, bulletin board approach, target position search mechanism, and position move method. Then, the adjustment strategy of search range of artificial fish, which combines the global search with local search, is proposed for improving the search ability of VAFSA. To testify the performance of VAFSA, three well-known case studies are tested with the comparison with other recently reported approaches. The experimental results show that VAFSA can obtain power saving compared with other approaches, and also with the competitive convergence ability. The proposed algorithm can be used as an attractive alternative method to operate air-conditioning systems. (C) 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Optimal chiller loading Energy saving artificial fish swarm algorithm

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