A GA-ACO Hybrid Algorithm for the Multi-UAV Mission Planning Problem

被引：0

作者：

Shang, Ke ^{[1
,2
]}

Karungaru, Stephen ^{[2
]}

Feng, Zuren ^{[1
]}

Ke, Liangjun ^{[1
]}

Terada, Kenji ^{[2
]}

机构：

[1] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Peoples R China

[2] Univ Tokushima, Dept Informat Sci & Intelligent Syst, Tokushima, Japan

来源：

2014 14th International Symposium on Communications and Information Technologies (ISCIT) | 2014年

关键词：

TEAM ORIENTEERING PROBLEM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Multi-UAV mission planning is a combinational optimization problem, that aims at planning a set of paths for UAVs to visit targets in order to collect the maximum surveillance benefits, while satisfying some constraints. In this paper, a genetic algorithm and ant colony optimization hybrid algorithm is proposed to solve the multi-UAV mission planning. The basic idea of the proposed hybrid algorithm is replacing the bad individuals of the GA's population by new individuals constructed by ant colony algorithm. Also, an efficient recombination operator called path relinking is used for mating. A population partition strategy is adopted for improving the evolving efficiency. Experimental results suggested that the proposed hybrid algorithm can solve the test instances effectively in a reasonable time. The comparison study with several existing algorithms shows that the proposed algorithm is competitive and promising.

引用

页码：243 / 248

页数：6

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