Abstract
Collision detection is an important problem in the field of computer graphics. In order to achieve efficient collision detection in large-scale object collections, this paper proposes a collision detection method based on the improved Whale Optimization Algorithm (WOA) and Axis-aligned Bounding Box (AABB). The method firstly determines the optimal enclosing box size to avoid repeated calculations; secondly, it uses AABB enclosing box to describe the geometrical information of the objects and initially detects whether the objects are intersecting or not, and then introduces Levy's flight strategy, adaptive weights, and adaptive learning factors into the optimization searching process of the improved whale optimization algorithm, which makes the collision detection method have stronger adaptivity and stability. Experiments show that the collision detection method based on the improved whale optimization algorithm has higher detection efficiency than the traditional method when dealing with a large-scale object collection, and the method exhibits superior optimization seeking ability compared with the traditional algorithm.
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Acknowledgements
The research was supported by the Industrial Science and Technology Research Project of Henan Province under Grants 232102210088, 232102210125, 222102210024.
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Yang, Z., Ma, J., Gu, P., Cui, J., Han, P. (2024). Collision Detection Method Based on Improved Whale Optimization Algorithm. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, Singapore. https://doi.org/10.1007/978-981-97-0903-8_25
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DOI: https://doi.org/10.1007/978-981-97-0903-8_25
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