引用本文:霍梦真,段海滨.基于自适应变异的多目标鸽群优化的无人机目标搜索[J].控制理论与应用,2020,37(3):584~591.[点击复制]
HUO Meng-zhen,DUAN Hai-bin.An adaptive mutant multi-objective pigeon-inspired optimization for unmanned aerial vehicle target search problem[J].Control Theory and Technology,2020,37(3):584~591.[点击复制]
基于自适应变异的多目标鸽群优化的无人机目标搜索
An adaptive mutant multi-objective pigeon-inspired optimization for unmanned aerial vehicle target search problem
摘要点击 1900  全文点击 748  投稿时间：2018-11-21  修订日期：2019-07-11
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DOI编号  10.7641/CTA.2019.80905
  2020,37(3):584-591
中文关键词  目标搜索  多目标鸽群优化算法  自适应飞行机制  变异机制
英文关键词  target search  multiobjective pigeon-inspired optimization  adaptive flight mechanism  mutant mechanism
基金项目  
作者单位E-mail
霍梦真 北京航空航天大学自动化学院自控系 mzhuo@buaa.edu.cn 
段海滨* 北京航空航天大学自动化学院自控系 hbduan@buaa.edu.cn 
中文摘要
      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。
英文摘要
      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 constrains 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 pigeoninspired 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 mutant mechanism is used to simplify the model of pigeon-inspired optimization (PIO) to improve the search efficiency. The experimental results validate the effectiveness of the proposed AMMOPIO algorithm in target search problem.