Solving the quadratic assignment problem with clues from nature

Author

Nissen, Volker

Author_Institution

Interdisziplinares Graduiertenkolleg, Gottingen, Germany

Volume

5

Issue

1

fYear

1994

fDate

1/1/1994 12:00:00 AM

Firstpage

66

Lastpage

72

Abstract

This paper describes a new evolutionary approach to solving quadratic assignment problems. The proposed technique is based loosely on a class of search and optimization algorithms known as evolution strategies (ES). These methods are inspired by the mechanics of biological evolution and have been applied successfully to a variety of difficult problems, particularly in continuous optimization. The combinatorial variant of ES presented here performs very well on the given test problems as compared with the standard 2-Opt heuristic and results with simulated annealing and tabu search. Extensions for practical applications in factory layout are described

Keywords

genetic algorithms; optimisation; search problems; combinatorial variant; evolution strategies; evolutionary approach; factory layout; optimization algorithms; quadratic assignment problem; search algorithms; Biological system modeling; Evolution (biology); Evolutionary computation; Genetic mutations; Optimization methods; Performance evaluation; Production facilities; Simulated annealing; Space exploration; Testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.265961

Filename

265961