摘要
无人机在搜索任务中起着关键的作用,它能够在复杂环境中寻找到目标.无人机搜索问题是一个相对复杂的多约束条件下的多目标优化问题.大多数搜索算法不能满足搜索过程中高效率和低功耗的要求.本文所采用的目标搜索方法是一种基于Agent路由和光传感器的解耦滚动时域方法.为了优化目标搜索方法的参数,本文提出一种基于Agent路由和光传感器的自适应变异多目标鸽群优化(AMMOPIO)算法.利用自适应飞行机制可以获得较好的鸽群分布,种群具有多样性和收敛性.利用变异机制简化了鸽群优化算法中的模型,提高了搜索效率.实验仿真结果验证了所提出的AMMOPIO算法在目标搜索问题中的可行性和有效性.
Unmanned aerial vehicle(UAV) is an indispensable tool for search missions, which can help find targets in critical and complex environments. The search problem of UAVs is a rather intricate multiobjective optimization problem with multiple constraints under complicated conflict environment. Most search algorithms could not meet the requirements of high efficiency and low consumption in combat environment. The target search approach employed in this paper is a decoupling receding horizon approach based on the agent routing and optical sensor tasking. To optimize the parameters of the target search approach, an adaptive mutant multiobjective pigeon-inspired optimization(AMMOPIO) algorithm is proposed for agent routing and optical sensor tasking optimization of target search problem. The utilization of adaptive flight mechanism could obtain the distribution of pigeons with applicable diversity and convergence. The mutation mechanism is used to simplify the model of pigeon-inspired optimization(PIO) to improve the search efficiency. The experimental results validate the feasibility and effectiveness of the proposed AMMOPIO algorithm in target search problem.
作者
霍梦真
段海滨
HUO Meng-zhen;DUAN Hai-bin(Bio-inspired Autonomous Flight Systems Research Group,School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,China;Peng Cheng Laboratory,Shenzhen Guangdong 518000,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2020年第3期584-591,共8页
Control Theory & Applications
基金
the National Natural Science Foundation of China(91648205).
关键词
目标搜索
多目标鸽群优化算法
自适应飞行机制
变异机制
target search
multi-objective pigeon-inspired optimization
adaptive flight mechanism
mutation mechanism
