基于自适应文化基因算法的无人机搜救规划

洪立斌; 王玥; 杜怡辰; 陈鑫; 郑宇军

doi:10.1631/FITEE.2000632

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基于自适应文化基因算法的无人机搜救规划

常规文章 | Updated：2022-01-05

- 基于自适应文化基因算法的无人机搜救规划
- UAV search-and-rescue planning using an adaptive memetic algorithm
- 信息与电子工程前沿（英文） 2021年22卷第11期页码：1477-1491
- Affiliations：
  
  School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
- Author bio：
  
  Yujun ZHENG, E-mail: yujun.zheng@computer.org
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61872123 and 61473263) and the Zhejiang Provincial Natural Science Foundation, China (No. LR20F030002)
- DOI：10.1631/FITEE.2000632
  中图分类号： TP399
- 收稿：2020-11-13，
  
  修回：2021-;9-;1，
  
  录用：2021-05-16，
  
  纸质出版：2021-11
- Accepted：
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洪立斌, 王玥, 杜怡辰, 等. 基于自适应文化基因算法的无人机搜救规划[J]. 信息与电子工程前沿（英文）, 2021,22(11):1477-1491.

Libin HONG, Yue WANG, Yichen DU, et al. UAV search-and-rescue planning using an adaptive memetic algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(11): 1477-1491.
洪立斌, 王玥, 杜怡辰, 等. 基于自适应文化基因算法的无人机搜救规划[J]. 信息与电子工程前沿（英文）, 2021,22(11):1477-1491. DOI： 10.1631/FITEE.2000632.

Libin HONG, Yue WANG, Yichen DU, et al. UAV search-and-rescue planning using an adaptive memetic algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(11): 1477-1491. DOI： 10.1631/FITEE.2000632.

摘要

无人机在搜救任务中的应用日益广泛，然而由于响应时间有限、搜索区域广、搜索模式多样，无人机搜索规划也更加复杂。本文提出一类无人机搜索规划问题，其搜索区域被划分为一组子区域，且每个子区域中目标存在的先验概率已知。解决该问题需要确定这些子区域的搜索顺序以及每个子区域的搜索模式，使得最终搜索成功的概率最大化。提出一种自适应文化基因算法，它结合了遗传算法和一组邻域搜索策略，基于问题求解过程中的适应度提升和多样性提升指标，动态选择邻域搜索策略。在多个问题实例上的计算实验表明，与先进的全局搜索启发式算法以及非自适应文化基因算法相比，所提算法展现了出色性能。

Abstract

The use of unmanned aerial vehicles (UAVs) is becoming more commonplace in search-and-rescue tasks

but UAV search planning can be very complex due to limited response time

large search area

and multiple candidate search modes. In this paper

we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics

non-adaptive memetic algorithms

and adaptive memetic algorithms on a wide set of problem instances.

关键词

Keywords

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