Evolutionary algorithms for the multiple unmanned aerial combat vehicles anti-ground attack problem in dynamic environments

Xingguang Peng, Shengxiang Yang, Demin Xu, Xiaoguang Gao

科研成果: 书/报告/会议事项章节章节同行评审

2 引用 (Scopus)

摘要

This chapter aims to solve the online path planning (OPP) and dynamic target assignment problems for the multiple unmanned aerial combat vehicles (UCAVs) anti-ground attack task using evolutionary algorithms (EAs). For the OPP problem, a model predictive control framework is adopted to continuously update the environmental information for the planner. A dynamic multi-objective EA with historical Pareto set linkage and prediction is proposed to optimize in the planning horizon. In addition, Bayesian network and fuzzy logic are used to quantify the bias value to each optimization objective so as to intelligently select an executive solution from the Pareto set. For dynamic target assignment, a weapon target assignment model that considers the inner dependence among the targets and the expected damage type is built up. For solving the involved dynamic optimization problems, an environment identification based memory scheme is proposed to enhance the performance of estimation of distribution algorithms. The proposed approaches are validated via simulation with a scenario of suppression of enemy air defense mission.

源语言英语
主期刊名Evolutionary Computation for Dynamic Optimization Problems
出版商Springer Verlag
403-431
页数29
ISBN(印刷版)9783642384158
DOI
出版状态已出版 - 2013

出版系列

姓名Studies in Computational Intelligence
490
ISSN(印刷版)1860-949X

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