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.
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