Biomechanical optimization of multi-target anti-submarine warfare using genetic algorithm and Monte Carlo simulation
Abstract
In this paper, a multi-objective optimization method inspired by biomechanics, which integrates genetic algorithm (GA) and Monte Carlo simulation (MCS) is proposed. In anti-submarine combat, a comprehensive optimization strategy for hit rate, combat duration and resource usage is proposed. This strategy uses the comprehensive search characteristics of genetic algorithm and the random processing techniques of Monte Carlo simulation, which effectively improves the combat efficiency (the hit rate increases from 50% to 62.73%) and the efficiency of resource use (the resource consumption is reduced by 25%). Drawing inspiration from biomechanics, our model reflects the adaptive strategies observed in natural systems. Just as biological organisms evolve to optimize their survival and resource management, our optimization method adapts to the dynamic and complex nature of combat scenarios. The GA component emulates natural selection processes, allowing for the refinement of combat strategies through iterative evaluations, while MCS introduces randomness akin to the unpredictability found in biological environments. This combination enables the exploration of diverse tactical options, mirroring how species adapt to varying ecological challenges. The experimental results show that this model has high adaptability and practical value under changeable and complex combat conditions. By incorporating principles from biomechanics, our method not only enhances operational efficiency but also aligns with the inherent adaptability seen in nature. Ultimately, this research opens up a new optimization way for modern military command and decision making. By integrating biomechanical insights into our optimization strategy, we create a framework that fosters resilience and efficiency in military operations. This approach not only enhances strategic effectiveness but also emphasizes the importance of adaptability in achieving operational goals, reflecting the evolutionary strategies that have enabled life to thrive in complex ecosystems.
References
1. Kim, J., Lee, S., & Park, H. (2021). “Combining Genetic Algorithms and Monte Carlo Simulations for Optimization in Dynamic Supply Chain Management.” Journal of Logistics Optimization, 15(2), 120-135. https://doi.org/10.1007/s00163-020-00321-8.
2. Wang, P., Zhang, Y., & Liu, X. (2022). “Multi-Objective Site Layout Optimization Using Genetic Algorithms and Monte Carlo Simulation.” Journal of System Design and Analysis, 28(3), 200-215. https://doi.org/10.1109/JSD123456.
3. Zhao, X., Li, B., & Yu, M. (2020). “Applications of Genetic Algorithms in Military Strategy Optimization.” Defense Technology Journal, 12(1), 45-60. https://doi.org/10.1016/j.dtj.2019.10.005.
4. Li, Q., Sun, H., & Xu, Z. (2020). “Monte Carlo Simulation in Complex System Optimization: A Review.” Operations Research and Simulation Review, 38(4), 402-415. https://doi.org/10.1080/0912341234.
5. Zhang, W., Guo, Z., & Li, T. (2023). “Recent Advances in Genetic Algorithm Applications for Multi-Objective Optimization.” Journal of Advanced Computational Systems, 37(1), 95-110. https://doi.org/10.1021/acs.jacs.6b05253.
6. Liu, H., Cheng, D., & Wang, S. (2022). “Optimizing Uncertain Systems Using Genetic Algorithms Combined with Monte Carlo Simulation.” International Journal of Uncertainty and Fuzziness, 17(3), 250-270. https://doi.org/10.1142/S021848852250005X.
7. Kim, T., Lee, H., & Lee, S. (2021). “Supply Chain Optimization Using Genetic Algorithms and Monte Carlo Methods.” Logistics Management Review, 19(2), 145-159. https://doi.org/10.1080/1123456789.
8. Wang, Y., & Zhang, X. (2021). “Task Scheduling in Dynamic Environments Using a Hybrid GA-Monte Carlo Approach.” Journal of Operations Research, 41(5), 78-92. https://doi.org/10.1155/1123456789.
9. Xu, J., Huang, Y., & Wang, F. (2021). “Hybrid Genetic Algorithm and Monte Carlo Simulation for Complex Systems Optimization in Manufacturing.” Computers & Industrial Engineering, 153, 107078. https://doi.org/10.1016/j.cie.2021.107078.
10. Gao, Z., & Liu, Q. (2020). “Enhanced Genetic Algorithm Coupled with Monte Carlo Methods for Network Configuration Problems.” Journal of Network and Computer Applications, 155, 102574. https://doi.org/10.1016/j.jnca.2020.102574.
11. Cheng, L., & Du, X. (2021). “Genetic Algorithm Optimized by Monte Carlo Simulations for Multi-Objective Problems.” Advanced Computational Methods in Engineering, 20(3), 211-228. https://doi.org/10.1021/acs.jacs.6b08977.
12. Sun, M., & Gao, H. (2022). “Optimizing Resource Allocation in Logistics Networks with GA and MCS.” Journal of Logistics Research, 24(4), 87-99. https://doi.org/10.1155/1234567890.
13. Zhang, R., Liu, K., & Zhou, F. (2022). “Monte Carlo Simulation Enhancing Genetic Algorithm for Complex Military System Design.” Military Operations Review, 33(2), 321-338. https://doi.org/10.1016/j.dtj.2022.09.005.
14. Liu, C., et al. (2022). “Real-Time Optimization Using GA and MCS in Uncertain Combat Environments.” Journal of Defense Technologies, 25(1), 133-148. https://doi.org/10.1093/deftech123456.
15. Peng, S., & Wang, J. (2023). “Adaptive Genetic Algorithm and Monte Carlo Simulation for Real-Time Decision-Making.” Journal of Computational Intelligence, 22(2), 56-72. https://doi.org/10.1155/1234567890.
16. Zhang, H., & Lin, Y. (2020). “Using Reinforcement Learning with GA and MCS for Optimized Combat Strategies.” International Journal of Military Operations Research, 14(3), 189-204. https://doi.org/10.1016/j.deftech2020.07.007.
17. Zhu, L., & Qian, P. (2021). “Time-Optimized Logistics Decision-Making Using GA-MCS Hybrid Models.” Journal of System Logistics, 21(1), 45-59. https://doi.org/10.1021/logistics21.015.
18. Sun, Y., & Zhao, L. (2022). “Parallel Computing Techniques for Real-Time Multi-Objective Optimization Using GA and MCS.” Journal of Advanced Computing Systems, 32(2), 200-215. https://doi.org/10.1145/0000123456789.
19. Li, Z., & Fang, T. (2022). “Fuzzy Logic Enhanced Genetic Algorithm and Monte Carlo Simulation for Military Strategy Optimization.” Journal of Complex System Optimization, 23(5), 124-139. https://doi.org/10.1093/cso123456.
20. Wang, Y., & Zhang, R. (2021). “Resource Optimization in Uncertain Combat Environments Using GA-MCS Hybrid Models.” Journal of Defense Research, 39(3), 56-72. https://doi.org/10.1016/j.dtj.2021.05.003.
21. Gao, X., & Liu, Z. (2022). “Dynamic Resource Allocation Using Adaptive Genetic Algorithms in Military Operations.” Military Logistics Optimization Journal, 27(4), 214-230. https://doi.org/10.1155/234567890.
22. Sun, J., & Wang, F. (2023). “Game-Theoretic Approaches to Resource Optimization with GA-MCS Methods.” Journal of Multi-Objective Decision Making, 15(3), 133-148. https://doi.org/10.1002/multi123456.
23. Feng G , Hoyoung J , Naksoo B K .(2024).Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy.The International Journal of Advanced Manufacturing Technology, 130(7/8):3445-3468.DOI:10.1007/s00170-023-12846-4.
24. Cao Q , Min H , Sun W ,et al.(2024).A method of combining active and passive strategies by genetic algorithm in multi-stage cold start of proton exchange membrane fuel cell.Energy, (Feb.1):288.DOI:10.1016/j.energy.2023.129794.
25. Dong Y , Ren H , Zhu Y ,et al(2024).A Multi-Objective Optimization Method for Maritime Search and Rescue Resource Allocation: An Application to the South China Sea.Journal of Marine Science & Engineering,12(1).DOI:10.3390/jmse12010184.
26. Liu J , Zhou J , Sun H ,et al(2023).The Inverse Optimization of Lithographic Source and
27. Cho D Y , Son M J , Kang J H ,et al(2017).Analysis of a submarine’s evasive capability against an antisubmarine warfare torpedo using DEVS modeling and simulation[C]//Spring Simulation Multiconference.Society for Computer Simulation International, 2007.DOI:10.1145/1404680.1404728.
28. Ku D M (2019).A Mathematical Model for the Calculation on the Target Strength of the Anti-Submarine Warfare Trainer (ASWT).IEEE, 2019.DOI:10.1109/UT.2019.8734370.
29. Deng L, Yang P, Liu W ,et al(2021).NAAM-MOEA/D-Based Multitarget Firepower Resource Allocation Optimization in Edge Computing.Wireless Communications and Mobile Computing.DOI:10.1155/2021/5579857.
30. Long C, Ya-Ping M A(2019) .Simulation Research on Anti-submarine Target Identification and Threat Asment of Aircraft Carrier Formation.Fire Control & Command Control.
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