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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References
Qichao, B., Min, L., Weijun, G., et al.: Study on collision detection techniques for the informed design of natural views in healthcare environments. HERD 15(3), 229–245 (2022)
Hu, Z., Qin, Q.: Minimum volume directed bounding box generation algorithm based on convex hull. J. Hunan Untiv. (Nat. Sci. Edn.) 46(2), 105–111 (2019)
Jin, Y., Cheng, Q., Zhang, J., Qi, X., Ma, B., Jia, Y.: Self-collision detection algorithm based on fused DNN and AABB-circular bounding box. J. Image Graph. 25(8), 1674–1683 (2020)
Wang, R., Hua, W., Xu, G.X., Huo, Y., Bao, H.: Variational hierarchica1 directed bounding box construction for solid mesh models. arXiv preprint arXiv (2022). 1–11 2203
Gan, B., Dong, Q.: An improved optimal algorithm for collision detection of hybrid hierarchica1bounding box. Evol. Intell. 2(1), 1–13 (2021)
Hui, X., Meng, X.: Research on virtual scenecollision detection based on bounding box intelligent algorithm. Comput. Simul. 38(7), 209–213 (2021)
Gan, B., Dong, Q.: An improved optimal algorithm for collision detection of hybrid hierarchical bounding box. Evol. Intell. 4(11), 1–13 (2021)
Huca, Y.E.J.: Clustering routing algorithm for wireless sensor networks with whale algorithm. Compet. Eng. Des. 40(11), 3067–3072 (2019)
Wang, T.: Trjectory optimization and control of grinding robot based on improved whale optimization algorithm. Taiyuan: North University of China (2021)
Yu, J., Liu, S., Wang, J., et al.: An ant-lionoptimization algorithm incorporating Levy flight andgolden sine. Comput. Appl. Res. 37(8), 2349–2353 (2020)
Zhang, Z.Z., He, X.S., Yu, Q.L., et al.: Cuckoo algorithm for muli-stage dynamic diturbance and dynamic inertia weight. Comput. Eng. Appl. 58(1), 79–88 (2022)
Mirjalilis, L.A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Jing, W., Xingyi, W., Xiongfei, L., et al.: A hybrid particle swarm optimization algorithm with dynamic adjustment of inertia weight based on a new feature selection method to optimize SVM parameters. Entropy 25(3), 531 (2023)
Du, Q., Zhen, C., Hao, H.: Fast collision detection algorithm based on quantum ant colony. Comput. Simul. 36(12), 209–213 (2019)
Duan, B., Ma, Y., Liu, J., Jin, Y.: A nonlinear gray wolf optimization algorithm based on chaotic mapping and backward learning mechanism. Softw. Eng. 26(05), 36–40 (2023)
Chen, L., Yin, J.S.: Whale swarm optimization algorithm based on Gaussian difference mutation and logarithmic inertia weight. In: Proceedings of the 57th International Conference on Computer Engineering and Applications (ICCEA), pp. 77–90 (2021). https://doi.org/10.3778/j.issn.1002-8331.2001-0290
Feng, W.T., Song, K.K.: An enhanced whale optimization algorithm. In: Proceedings of the 37th International Conference on Computer Simulation (CSIM), pp. 275–279 (2020). https://doi.org/10.3969/j.issn.1006-9348.2020.11.057
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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