Research on group animation design technology based on artificial fish swarm algorithm

Abstract

Group animation is a technology that simulates group behavior by computer. It matures with the continuous development of computer graphics. The things that need to be described are expressed in the form of anime, which not only clearly reflects the process of the event, but also shows a more realistic picture, and can also increase the cognitive range of group behavior to a certain extent. Based on the analysis of the advantages and disadvantages of artificial fish swarm algorithm, this paper improves the research on artificial fish swarm algorithm, and it is easy to fall into local optimum, and it is improved by using convergence and divergence behavior in social learning mechanism., to some extent, improve the search performance of the algorithm. Based on the collision detection and avoidance, the improved artificial fish swarm algorithm is used to plan the group behavior path, and the obtained path data is imported into Maya. The tools provided by Maya will be used to establish the characters, 3D scenes and planning. The paths are merged together to get the corresponding group animation effects.

Keywords

Artificial fish swarm algorithm group animation design intelligent algorithm 3D scene

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References

Lin

, Zhang

and Liu

, Parameters optimization of gm (1, 1) model based on artificial fish swarm algorithm, Grey Systems 2(2) (2012), 166–177.

Shuang

L.I.

, Wenjing

L.I.

, Sun

and Lin

, Artificial fish swarm parallel algorithm based on multi-core cluster, Journal of Computer Applications 33(12) (2013), 3380–3384.

Sun

, Hui

, Liu

and Cao

, Application of an artificial fish swarm algorithm in solving multiobjective trajectory optimization problems. Chemistry & Technology of Fuels & Oils 53(4) (2017), 541–547.

Duan

, Mao

, Pan

and Bei

, An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory. Kybernetes, 45(2) (2016), 210–222.

Shaofei

, A Traffic Motion Object Extraction Algorithm, International Journal of Bifurcation and Chaos 25(14) (2015), Articl Number 1540039.

Shaofei

, Mingqing

and Yuntao

, Research on internet information mining based on agent algorithm, Future Generation Computer Systems 86 (2018), 598–602.

Shaofei

, Nonlinear information data mining based on time series for fractional differential operators, Chaos 29 (2019), 013114. DOI: 10.1063/1.5085430.

Wang

H.B.

, Fan

C.C.

and Tu

X.Y.

, Afsaocp: a novel artificial fish swarm optimization algorithm aided by ocean current power, Applied Intelligence 45(4) (2016), 1–16.

M.A.

, Yang

L.I.

, Fan

S.H.

and Mathematics

D.O.

, Application of the improved artificial fish swarm algorithm in foreign exchange forecast and portfolio, Systems Engineering-Theory & Practice 35(5) (2015), 1256–1266.

10.

Azad

M.A.K.

, Rocha

A.M.A.C.

and Fernandes

E.M.G.P.

, Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm, Journal of Mathematical Modelling & Algorithms in Operations Research 14(3) (2015), 313–330.

11.

Gao

, Guan

, Wang

and Sun

, A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters, Sensors 15(5) (2015), 10547–10568.

12.

Hao

, Zhao

and Zhang

, Development of a distributed artificial fish swarm algorithm to optimize pumps working in parallel mode, Science & Technology for the Built Environment 24(30) (2017), 1–11.

13.

Leng

, Jing

, Lin

, Chang

and Lam

, Research on mining collaborative behaviour patterns of dynamic supply chain network from the perspective of big data, 31(S1) (2019), 113–121.

14.

Ashit

K.D.

, Mohamed

, Vandna

and Shankar

, An efficient hierarchical clustering protocol for multihop Internet of vehicles communication, Transactions on Emerging Telecommunications Technologies (2019). In Press. DOI: 10.1002/ett.3690

15.

Mohamed

, Shankar

and Uthayakumar

, Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease, Nature Scientific Reports, July 2019. In Press. DOI: 10.1038/s41598-019-46074-2

16.

Mohamed

and Shankar

, Reliable Data Transmission Model for Mobile Ad Hoc Network Using Signcryption Technique, IEEE Transactions on Reliability (June 2019), 1-10. In Press. DOI: 10.1109/TR.2019.2915800

17.

Krishnaraj

, Mohamed

, Thenmozhi

, Mahmoud

M.S.

and Shankar

, “Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)”, Journal of Real-Time Image Processing (2019). In Press. DOI: 10.1007/s11554-019-00879-6

18.

Mohamed

and Shankar

, “Optimal Bilateral Filter and Convolutional Neural Network based Denoising Method of Medical Image Measurements”, Measurement 143 (2019), 125–135.

19.

Ragavan

V.K.S.

, Mohamed

and Shankar

, “An Enhanced Whale Optimization Algorithm for Vehicular Communication Networks”, International Journal of Communication Systems April 2019. DOI: 10.1002/dac.3953

20.

Shankar

, Elhoseny

Mohamed

, Eswaran Perumal

, Ilayaraja

and Sathesh Kumar , An Efficient Image Encryption Scheme Based on Signcryption Technique with Adaptive Elephant Herding Optimization, Cybersecurity and Secure Information Systems: Challenges and Solutions in Smart Environments, Advanced Sciences and Technologies for Security Applications, ISBN: 978-3-030-16837-7 (2019), 31–42.