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5th International Conference on swarm Intelligence (ICSI)

作者： Wang, Fei Xu, Jiao Li, Lian Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9783319118970;9783319118963

With the purpose of finding the minimal reduct, this paper proposes a novel feature selection algorithm based on artificial fish swarm algorithm (AFSA) hybrid with rough set (AFSARS). The proposed algorithm searches the minimal reduct in an efficient way to observe the change of the significance of feature subsets and the number of selected features, which is experimentally compared with the quick reduct and other hybrid rough set methods such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO) and chaotic binary particle swarm optimization (CBPSO). Experiments demonstrate that the proposed algorithm could achieve the minimal reduct more efficiently than the other methods.

关键词： feature selection rough set fish swarm algorithm ant colony optimization chaotic binary particle swarm optimization

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Improved Artificial fish swarm algorithm 9

Improved Artificial Fish Swarm Algorithm

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9th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang Chao Zhang Feng-ming Li Fei Wu Hu-sheng Air Force Engn Univ Mat Management & Safety Engn Inst Xian Peoples R China Armed Police Force Engn Univ Mat Engn Inst Xian Peoples R China

ISBN: (纸本)9781479943159

Artificial fish swarm algorithm is a kind of swarm intelligence algorithm that imitates the behavior of individuals and information interactions among them during feeding process in real environment. This paper proposes an improved artificial fish swarm algorithm. First of all, it adopts dynamic adjustments for the view and step length of artificial fish, which let it keep a large value during the early stage of the algorithm. The value will change from big to small and thus it realizes the transformation from rough search to refined search. Secondly, let the artificial fish move several steps towards a better position during the foraging, bunching and rear-end behaviors and avoid repeatedly searching the same regions for the feeding behavior by introducing the concepts of decay factor as well as information uncertainty of searching regions, which will speed up the convergence. Finally, improve the update strategy of the call-board by comparing the food density in historical best position of each individual artificial fish with that of the partner's in the group or nearby regions and pick out the best to replace each other in order to make the group develop towards a better direction. Simulation result shows that the convergence performance of the improved algorithm is much better than that of the original one's.

关键词： Artificial fish swarm swarm intelligence fish swarm algorithm Food density Foraging behavior

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Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Goli, Alireza Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan *** Iran

In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial fish swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.

关键词： Renewable energy project portfolio Profitability Investment risk Artificial immune system algorithm fish swarm algorithm

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MO-NFSA for solving unconstrained multi-objective optimization problems

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ENGINEERING WITH COMPUTERS 2022年第3期38卷 2527-2548页

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

Normative fish swarm algorithm (NFSA) is a novel variant of the Artificial fish swarm algorithm (AFSA) proposed in 2019, and has been proven effective in solving single-objective optimization problems. Inspired by the potential of NFSA, this paper proposes an evolutionary multi-objective optimization algorithm, namely "Multi-Objective Normative fish swarm algorithm (MO-NFSA)". However, due to the fact that NFSA was originally modeled for single-objective optimization, a certain degree of transformation, modification, and additional strategies (related to multi-objective optimization features) must be integrated into the original NFSA as part of MO-NFSA modeling for further extension. This article adopts a total of 15 multi-objective optimization test cases in any category of fixed-dimensional, non-fixed-dimensional (ZDT set) or scalable multi-objective (DTLZ set) optimization types. These multi-objective optimization test cases are used to compare the performance of MO-NFSA with other comparative algorithms. However, performance evaluation of multi-objective optimization is a daunting task. Their performance results can only be digitized through quality indicators (i.e., performance metrics), which are mainly tested from three different aspects of high-quality approximation: convergence, uniformity, and spread. Here, quality indicators including generation distance (GD), spacing (S), and spread-delta ( increment / increment *) are used to check the quality performance of each algorithm based on the corresponding approximation. In the work, 20 simulations are run, and hence, 20 sets of data are collected. The collected results prove that MO-NFSA is superior to other comparison algorithms in all aspects of high-quality approximation. MO-NFSA can solve different types of multi-objective optimization problems.

关键词： fish swarm algorithm Multi-objective optimization Pareto front Performance metric Unconstrained test function

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Study of Computer Scanning Electron Microscopy Technique in Sports Injury Recovery System 6

Study of Computer Scanning Electron Microscopy Technique in ...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Ma, Lin Taishan University College of Physical Education Tai'an City271000 China

ISBN: (纸本)9798350313444

A monitoring system for sports injury rehabilitation was designed by using electron microscope scanning technology. In this paper, the structure and function of mobile injury risk warning system are studied. The system hardware is designed by frame design and synchronization module design, and the system software is designed by structure design and parameter setting. The fish swarm algorithm is used to analyze the high-intensity sports injury. Firstly, the edge of the damaged area is extracted from the motion damage image, and then the basic identification results of the damage image are obtained. It can be seen from the test results that the monitoring system has a high precision of monitoring the damage type, and the resistance value is not different from the measured value. © 2023 IEEE.

关键词： Electron microscope scanning fish swarm algorithm motor recovery sports injury sports injury monitoring system

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Optimisation of German Language Database Query for Foreign Companies Based on Hybrid Learning

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2022年第SUPP02期21卷

作者： Yan, Chengcheng Xian Mingde Inst Technol Coll Languages & Culture Commun Xian 710124 Peoples R China

Traditional database query optimisation methods use stochastic algorithms to approximate the query optimisation results by continuously adjusting the optimisation plan. Since the stochastic algorithm only performs query optimisation from a single perspective, it leads to no significant improvement of the optimised database query efficiency. To address the above problems, we studied the query optimisation method of foreign enterprises' German language data database based on hybrid learning. By reducing the database query search space and selecting query optimisation strategy, the data query complexity is reduced. After estimating the cost of database query optimisation, the policy selection algorithm is trained using the hybrid learning theory to obtain the database query optimisation path. The simulation experimental results show that the average query response of the optimised database after applying the studied method saves about 13.6%, and the query cost is lower and the optimisation effect is better.

关键词： Hybrid learning German language materials for foreign companies database query optimisation methods fish swarm algorithm

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fish swarm Based Man-Machine Cooperative Photographing Location Positioning algorithm 15th

Fish Swarm Based Man-Machine Cooperative Photographing Locat...

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15th International Conference on Cooperative Design, Visualization, and Engineering (CDVE)

作者： Zang, Zelin Wang, Wanliang Lu, Linyan Zhao, Yanwei Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783030005603;9783030005597

This paper establishes a man-machine cooperation system to extract hidden shooting location information from the image data. The system extracts shadow position information (such as the estimated distance between the camera and the object) with the help of humans. Camera Imaging Model (CIM) is created with the knowledge of earth astronomy. This model maps latitude, longitude, and date factors to shadow space. Solar Projection Model (SPM) is established with the pinhole imaging principle. In the shadow space, a swarm intelligence algorithm, fish swarm algorithm, is used to minimizes the location error. The results show that the system can achieve effective extraction of picture depth information. Our algorithm can reduce the solution error to less than 20km within 30 s.

关键词： Video shooting point location fish swarm algorithm Planning problem Numerical calculation

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ON THE METAHEURISTIC OPTIMIZATION algorithmS IN THE STRUGGLE FOR THE HOT FLOW CURVE APPROXIMATION ACCURACY 29

ON THE METAHEURISTIC OPTIMIZATION ALGORITHMS IN THE STRUGGLE...

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29th International Conference on Metallurgy and Materials (METAL)

作者： Opela, Petr Schindler, Ivo Rusz, Stanislav Sevcak, Vojtech Mamuzic, Ilija VSB Tech Univ Ostrava Ostrava Czech Republic Univ Zagreb Fac Met Sisak Croatia

ISBN: (纸本)9788087294970

A hot flow curve approximation performed via flow stress models as well as artificial neural networks requires precisely estimated constants. This estimation is in the case of highly-nonlinear issues often solved via gradient optimization algorithms. Nevertheless, by natural processes or physical laws inspired approaches (metaheuristic algorithms) are also of high interest. In the submitted manuscript, three selected metaheuristic algorithms were compared under the approximation of an experimental hot flow curve dataset via the well-known Hensel-Spittel relationship. One often used gradient algorithm was also included into this comparison. Results have showed that the metaheuristic algorithms are useful if such complex approximation model is applied and no estimate of material constants from a previous approximation issue is used. On the other hand, if this estimation exists, the gradient algorithms should provide a better solution.

关键词： Hot-flow-curve approximation genetic algorithm artificial bee colony fish swarm algorithm

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fish swarm window selection algorithm based on cell microscopic automatic focus

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2017年第1期20卷 485-495页

作者： Zhang, Feng-shou Li, Si-wen Hu, Zhi-gang Du, Zhe Henan Univ Sci & Technol Sch Mechatron & Engn Luoyang 471003 Henan Peoples R China Henan Univ Sci & Technol Sch Med Technol & Engn Luoyang 471003 Henan Peoples R China

The selection window of selection algorithm used in traditional automatic focus window concentrates mainly on the center of image, so the randomly distributed cells would always be out of focus. To address this problem, on the basis of analyzing the performance of selection algorithm of different focusing windows, a modified auto-focus window algorithm upon traditional fish swarm algorithm has been proposed: fish swarm window selection algorithm. After comparatively analyzing the images in focus window that are obtained by traditional and improved fish swarm algorithm, a conclude can be drawn that the focus window of modified algorithm can contain more cells and target bodies. To be specific, owing to fish-swarm window selection algorithm, in the selection window the quantity of the high frequency of images greatly increases, the optimal solution converges to 0.999, and the estimated value of sharpness of the obtained microscopic cell images also improves with high precision and high accuracy of focus of the improved algorithm.

关键词： Focusing window Cell images processing fish swarm algorithm Auto-focusing fish swarm window selection algorithm

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Research on Flight First Service Model and algorithms for the Gate Assignment Problem

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Computers, Materials & Continua 2019年第9期61卷 1091-1104页

作者： Jiarui Zhang Gang Wang Siyuan Tong Graduate School Air Force Engineering UniversityXi’an710051China Air and Missile Defense College Air Force Engineering UniversityXi’an710051China

Aiming at the problem of gate allocation of transit flights,a flight first service model is *** the constraints of maximizing the utilization rate of gates and minimizing the transit time,the idea of“first flight serving first”is used to allocate the first time,and then the hybrid algorithm of artificial fish swarm and simulated annealing is used to find the optimal *** means the fish swarm algorithm with the swallowing behavior is employed to find the optimal solution quickly,and the simulated annealing algorithm is used to obtain a global optimal allocation scheme for the optimal local *** experimental data show that the maximum utilization of the gate is 27.81%higher than that of the“first come first serve”method when the apron is not limited,and the hybrid algorithm has fewer iterations than the simulated annealing algorithm alone,with the overall passenger transfer tension reducing by 1.615;the hybrid algorithm has faster convergence and better performance than the artificial fish swarm algorithm *** experimental results indicate that the hybrid algorithm of fish swarm and simulated annealing can achieve higher utilization rate of gates and lower passenger transfer tension under the idea of“first flight serving first”.

关键词： Gate assignment flight first service model fish swarm algorithm passenger transfer tension

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