Research on Cooperative Task Allocation for Multiple UCAVs Based on Modified Co-evolutionary Genetic Algorithm

Author

Mingren Xu ; Shewei Wang ; Jun Tao ; Guowei Liang

Author_Institution

Dept. of Aviation Control Eng., Aviation Univ. of Air Force, Changchun, China

fYear

2013

fDate

21-23 June 2013

Firstpage

125

Lastpage

128

Abstract

Uninhabited combat aerial vehicles (UCAVs) task allocation problem is one of the most important issues in UCAV research. This paper refers to an extension of the mixed-integer linear programming (MILP) task allocation model. Through analyzing the model characteristics, and combining with the advantages and disadvantages of co-evolutionary genetic algorithm, this paper modifies the mechanism of search space and evolution among populations in co-evolutionary genetic algorithm (MCOGA) to improve the convergence and diversity of algorithm. At last, the feasibility and effectiveness of the modified algorithm in solving the problem of multiple UCAVs task allocation is verified by simulation.

Keywords

autonomous aerial vehicles; genetic algorithms; integer programming; linear programming; military aircraft; mobile robots; multi-robot systems; path planning; MCOGA; MILP task allocation model; UCAV research; algorithm convergence; algorithm diversity; cooperative task allocation; mixed integer linear programming; modified co-evolutionary genetic algorithm; multiple UCAV; search space mechanism; uninhabited combat aerial vehicles; Encoding; Genetic algorithms; Optimization; Partitioning algorithms; Resource management; Sociology; Statistics; mixed-integer linear programming; modified co-evolution genetic algorithm; multiple UCAVs; task allocation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on

Conference_Location

Shiyang

Type

conf

DOI

10.1109/ICCIS.2013.41

Filename

6642956