Solving the multi-objective quadratic assignment problem using a fast messy genetic algorithm

Author

Day, Richard O. ; Kleeman, Mark P. ; Lamont, Gary B.

Author_Institution

Dept. of Electr. & Comput. Eng., Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA

Volume

4

fYear

2003

fDate

8-12 Dec. 2003

Firstpage

2277

Abstract

The multiobjective quadratic assignment problem is an NP-complete problem with a multitude of real-world applications. The specific application addressed is the minimization of communication flows in a heterogenous mix of unmanned aerial vehicles. Developed is a multiobjective approach to solving the general mQAP for this UAV application. The combinatoric nature of this problem calls for a stochastic search algorithm; moreover, the multiobjective fast messy genetic algorithm (MOMGA-II) [Jesse Zydallis (2003)] is used for experimentation. Results indicate that much of the Pareto optimal points are found.

Keywords

Pareto optimisation; genetic algorithms; minimisation; operations research; quadratic programming; remotely operated vehicles; stochastic programming; NP-complete problem; Pareto optimal points; communication flow minimization; fast messy genetic algorithm; multiobjective quadratic assignment problem; real-world applications; stochastic search algorithm; unmanned aerial vehicles; Aircraft; Application software; Combinatorial mathematics; Computer hacking; Evolutionary computation; Genetic algorithms; NP-complete problem; Protection; Reconnaissance; Unmanned aerial vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on

Print_ISBN

0-7803-7804-0

Type

conf

DOI

10.1109/CEC.2003.1299372

Filename

1299372