Demon algorithms and their application to optimization problems

Author

Wood, Ian ; Downs, Tom

Author_Institution

Dept. of Electr. & Comput. Eng., Queensland Univ., Qld., Australia

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

1661

Abstract

We introduce four new general optimization algorithms based on the `demon´ algorithm from statistical physics and the simulated annealing (SA) optimization method. These algorithms reduce the computation time per trial without significant effect on the quality of solutions found. Any SA annealing schedule or move generation function can be used. The algorithms are tested on traveling salesman problems including Grotschel´s 442-city problem (1984) with results comparable to SA. Applications to the Boltzmann machine are considered

Keywords

computational complexity; neural nets; simulated annealing; travelling salesman problems; 442-city TSP; Boltzmann machine; SA; computation time; demon algorithms; move generation function; optimization problems; simulated annealing; statistical physics; traveling salesman problems; Computational modeling; Optimization methods; Physics; Processor scheduling; Recurrent neural networks; Sampling methods; Simulated annealing; Temperature; Testing; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.686028

Filename

686028