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Multi-Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

机译:基于改进分解进化算法的多目标罢工路径规划

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摘要

This study proposes a path-finding model for multi-target strike planning. The model evaluates three elements, i.e., the target value, the aircraft’s threat tolerance, and the battlefield threat, and optimizes the striking path by constraining the balance between mission execution and the combat survival. In order to improve the speed of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), we use the conjugate gradient method for optimization. A Gaussian perturbation is added to the search points to make their distribution closer to the population distribution. The simulation shows that the proposed method effectively chooses its target according to the target value and the aircraft’s acceptable threat value, completes the strike on high value targets, evades threats, and verifies the feasibility and effectiveness of the multi-objective optimization model.
机译:本研究提出了一种用于多目标冲击计划的路径发现模型。该模型评估三个元素,即目标价值,飞机的威胁容忍以及战场威胁,并通过限制执行任务执行和战斗生存之间的平衡来优化引人注目的路径。为了提高基于分解(MOEA / D)的多目标进化算法的速度,我们使用共轭梯度法进行优化。将高斯扰动添加到搜索点中,以使其分布更接近人口分布。模拟表明,该方法根据目标值和飞机的可接受威胁值有效地选择其目标,完成高价值目标的罢工,避开威胁,并验证多目标优化模型的可行性和有效性。

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