基于APMO-HV算法的导弹路径规划优化

Hu Zhang, Shuai Wang, Tonglin Liu, Aimin Zhou, Yi Zhang
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引用次数: 0

摘要

本文针对传统聚合技术在导弹路径规划(MPP)中效率较低的问题,在建立了导弹路径规划模型后,采用基于亲和传播的hypervolume环境选择多目标进化算法(APMO-HV)解决导弹路径规划问题。实验部分对APMO-HV算法与六种最先进的算法进行了比较分析,并将其应用于MPP问题。实验结果表明,与其他6种算法相比,APMO-HV算法在GLT测试套件和MPP问题上都取得了最佳的求解性能。这不仅验证了所提算法的效果,而且丰富和完善了MPP的研究成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Missile Path Planning Based on APMO-HV Algorithm
In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP.
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