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- W4386324062 abstract "With the growth of communication technology and hardware technology, animation production can only meet the needs of the times by continuous innovation. Aiming at the problems of traditional animation, such as single method, heavy task and low autonomy, the particle swarm optimization (PSO) algorithm is improved by using the convergence and divergence behavior in social learning mechanism. In order to improve the production method of group animation, this paper applies the modified PSO algorithm to the group animation production system driven by artificial intelligence, improves the influence mode of the “social” part of the standard PSO algorithm on particle motion, and selects the particle with the best fitness value in the particle field of view as the global extremum of the particle, so as to overcome the shortcoming that the original algorithm adopts the best individual among all particles as the global extremum and easily falls into the local optimum, thus simulating the group behavior more truly. The simulation results show that after the algorithm is iterated for a certain quantity of times, the particles tend to converge, but due to the limitation of the field of view, the quantity of connections between particles will remain in a relatively stable state, and the particles will not fall into the local optimum because of excessive aggregation, so it has the ability to continue exploration. PSO algorithm can truly simulate group behavior, which provides a basis for the design of group animation production system." @default.
- W4386324062 created "2023-09-01" @default.
- W4386324062 creator A5022750589 @default.
- W4386324062 date "2023-06-01" @default.
- W4386324062 modified "2023-09-27" @default.
- W4386324062 title "Design of a Group Animation Production System Based on Artificial Intelligence Algorithms" @default.
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- W4386324062 doi "https://doi.org/10.1109/icdiime59043.2023.00103" @default.
- W4386324062 hasPublicationYear "2023" @default.
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