Study on Time-Sensitive Targets Strike Path Planning Based on Improved Crayfish Optimization Algorithm

Yalong Wang, Xianming Shi, Mifen Yang, Penghua Liu, Qian Zhao
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:696-706, 2025.

Abstract

Addressing the challenges of complex solution and low accuracy in time-sensitive targets strike path planning, this paper proposes a novel path planning method which, based on the Open Vehicle Path Problem (OVRP), builds a model and applies the Improved Crayfish Optimization Algorithm (ICOA) to solving it. Relative to the initial Crayfish Optimization Algorithm (COA), the ICOA employs an improved strategy, namely “Chaos Accumulation-Environment Awareness-Lens Imaging” to markedly enhance the optimization efficiency and robustness of the algorithm and, through integer coding and crossover operation, is integrated with a Genetic Algorithm (GA) and innovatively applied to addressing the OVRPs. The experimental results demonstrate that ICOA exhibits better convergence speed and optimization accuracy over the other algorithms in composite optimization, displays enhanced robustness, and is capable of rapidly generating a path planning scheme with a shorter total flight distance in the OVRP model, further verifying the effectiveness of ICOA in solving the OVRPs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-wang25h, title = {Study on Time-Sensitive Targets Strike Path Planning Based on Improved Crayfish Optimization Algorithm}, author = {Wang, Yalong and Shi, Xianming and Yang, Mifen and Liu, Penghua and Zhao, Qian}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {696--706}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/wang25h/wang25h.pdf}, url = {https://proceedings.mlr.press/v278/wang25h.html}, abstract = {Addressing the challenges of complex solution and low accuracy in time-sensitive targets strike path planning, this paper proposes a novel path planning method which, based on the Open Vehicle Path Problem (OVRP), builds a model and applies the Improved Crayfish Optimization Algorithm (ICOA) to solving it. Relative to the initial Crayfish Optimization Algorithm (COA), the ICOA employs an improved strategy, namely “Chaos Accumulation-Environment Awareness-Lens Imaging” to markedly enhance the optimization efficiency and robustness of the algorithm and, through integer coding and crossover operation, is integrated with a Genetic Algorithm (GA) and innovatively applied to addressing the OVRPs. The experimental results demonstrate that ICOA exhibits better convergence speed and optimization accuracy over the other algorithms in composite optimization, displays enhanced robustness, and is capable of rapidly generating a path planning scheme with a shorter total flight distance in the OVRP model, further verifying the effectiveness of ICOA in solving the OVRPs.} }
Endnote
%0 Conference Paper %T Study on Time-Sensitive Targets Strike Path Planning Based on Improved Crayfish Optimization Algorithm %A Yalong Wang %A Xianming Shi %A Mifen Yang %A Penghua Liu %A Qian Zhao %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-wang25h %I PMLR %P 696--706 %U https://proceedings.mlr.press/v278/wang25h.html %V 278 %X Addressing the challenges of complex solution and low accuracy in time-sensitive targets strike path planning, this paper proposes a novel path planning method which, based on the Open Vehicle Path Problem (OVRP), builds a model and applies the Improved Crayfish Optimization Algorithm (ICOA) to solving it. Relative to the initial Crayfish Optimization Algorithm (COA), the ICOA employs an improved strategy, namely “Chaos Accumulation-Environment Awareness-Lens Imaging” to markedly enhance the optimization efficiency and robustness of the algorithm and, through integer coding and crossover operation, is integrated with a Genetic Algorithm (GA) and innovatively applied to addressing the OVRPs. The experimental results demonstrate that ICOA exhibits better convergence speed and optimization accuracy over the other algorithms in composite optimization, displays enhanced robustness, and is capable of rapidly generating a path planning scheme with a shorter total flight distance in the OVRP model, further verifying the effectiveness of ICOA in solving the OVRPs.
APA
Wang, Y., Shi, X., Yang, M., Liu, P. & Zhao, Q.. (2025). Study on Time-Sensitive Targets Strike Path Planning Based on Improved Crayfish Optimization Algorithm. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:696-706 Available from https://proceedings.mlr.press/v278/wang25h.html.

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