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Research on Intelligent Minefield Attack Decision Based on Adaptive Fireworks Algorithm

机译：基于自适应烟花算法的智能雷场攻击决策研究

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摘要

The decision of intelligent minefield attacking tank forces is a complex multi-constraint and multi-objective nonlinear optimization problem. Aiming at the common defects of commonly used intelligent algorithms and combining with the characteristics of fireworks algorithm, this paper proposed an adaptive fireworks algorithm to deal with it. In this paper, we first established the mathematical model of this problem and transformed the model into an unconstrained single-objective extremum by using the external penalty function method. Furthermore, the adaptive fireworks algorithm is used to solve the model. In order to verify the superiority of adaptive fireworks algorithm to deal with this problem, experimental results show that the adaptive fireworks algorithm has faster convergence speed and shorter computation time than the other algorithms, and the results can intuitively describe the reasonable task allocation scheme of the complex situation, which provides a foundation for studying the force control.
机译：智能雷场攻击坦克部队的决策是一个复杂的多约束多目标非线性优化问题。针对常用智能算法的普遍缺陷，结合烟花算法的特点，提出了一种自适应烟花算法。在本文中，我们首先建立了这个问题的数学模型，并使用外部惩罚函数方法将其转换为无约束的单目标极值。此外，使用自适应烟花算法来求解模型。为了验证自适应烟花算法在处理该问题上的优越性，实验结果表明，自适应烟花算法比其他算法具有更快的收敛速度和更短的计算时间，可以直观地描述自适应烟花算法的合理任务分配方案。复杂的情况，为研究力控制提供了基础。

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