Learning-guided coevolution multi-objective particle swarm optimization for heterogeneous UAV cooperative multi-task reallocation problem

被引：0

作者：

Wang F. ^{[1
]}

Fu Q.-P. ^{[1
]}

Han M.-C. ^{[1
]}

Xing L.-N. ^{[2
]}

Wu H.-S. ^{[3
]}

机构：

[1] School of Computer Science, Wuhan University, Hubei, Wuhan

[2] College of System Engineering, National University of Defense Technology, Hunan, Changsha

[3] School of Equipment Management and Support, Armed Police Force Engineering University, Shaanxi, Xi’an

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 06期

关键词：

coevolution; multi-objective optimization; particle swarm optimization; UAV multi-task reallocation;

D O I：

10.7641/CTA.2023.20665

中图分类号：

学科分类号：

摘要：

UAV system has been widely used in military field. Due to the complex and changeable battlefield environment, UAV tasks need to be reassigned after an emergency. Heterogeneous UAVs refer to multiple types of UAVs, which can accomplish multiple types of complex tasks that a single UAV can not. The heterogeneous UAV cooperative multi-task reallocation problem has complex constraints and mixed variables, and the existing multi-objective optimization algorithms can not deal with this kind of problems effectively. In order to solve the above problems efficiently, a multi-constraint heterogeneous UAVs cooperative multi-task reallocation model is constructed at first in this paper, and a learning-guided cooperative multi-objective particle swarm optimization algorithm (LeCMPSO) is proposed to solve that. In LeCMPSO, a prior knowledge based initialization strategy as well as a history information learning based particle update strategy are introduced to avoid the generation of infeasible solutions and improve the search efficiency of the algorithm. The simulation results on 4 sets of examples show that the proposed algorithm outperforms the other typical coevolutionary multi-objective optimization algorithms on diversity of solution sets, convergence, and search time. © 2024 South China University of Technology. All rights reserved.

引用

页码：1009 / 1017

页数：8

共 23 条

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