A Boolean neural network approach for the traveling salesman problem

Author

Bhide, Shirish ; John, Nigel ; Kabuka, M.R.

Author_Institution

Dept. of Electr. & Comput. Eng., Miami Univ., Coral Gables, FL, USA

Volume

42

Issue

10

fYear

1993

fDate

10/1/1993 12:00:00 AM

Firstpage

1271

Lastpage

1278

Abstract

It is shown that the Boolean-neural network can be used to solve NP-complete problems. The problem under consideration is the traveling salesman problem. The Boolean neural network has been modified to include the iterative procedure for solving combinatorial optimization problems. An architecture that utilizes this modified Boolean neural network (MBNN) is proposed for solving this problem. The simulation results have been found to be comparable to the simulated annealing algorithm (SAA), which is used as a test base. The MBNN implementation involves low hardware complexity, good noise immunity, and fast circuitry. This is very important in real-time systems and commercial job scheduling applications

Keywords

Boolean functions; computational complexity; neural nets; real-time systems; scheduling; simulated annealing; Boolean neural network; NP-complete problems; combinatorial optimization; hardware complexity; job scheduling; noise immunity; real-time systems; simulated annealing algorithm; simulation; traveling salesman problem; Circuit noise; Circuit simulation; Circuit testing; Hardware; Iterative algorithms; NP-complete problem; Neural networks; Real time systems; Simulated annealing; Traveling salesman problems;

fLanguage

English

Journal_Title

Computers, IEEE Transactions on

Publisher

ieee

ISSN

0018-9340

Type

jour

DOI

10.1109/12.257714

Filename

257714