A Boolean Neural Network Approach for the Traveling Salesman Problem

Shirish Bhide, Nigel John, Mansur R. Kabuka

Research output: Contribution to journalArticle

27 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1271-1278
Number of pages8
JournalIEEE Transactions on Computers
Volume42
Issue number10
DOIs
StatePublished - 1993

Fingerprint

Boolean Networks
Traveling salesman problem
Travelling salesman problems
Neural Networks
Neural networks
Job Scheduling
Simulated Annealing Algorithm
Combinatorial optimization
Immunity
Iterative Procedure
Real time systems
Simulated annealing
Combinatorial Optimization Problem
Computational complexity
NP-complete problem
Scheduling
Hardware
Real-time
Simulation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Software
  • Theoretical Computer Science

Cite this

A Boolean Neural Network Approach for the Traveling Salesman Problem. / Bhide, Shirish; John, Nigel; Kabuka, Mansur R.

In: IEEE Transactions on Computers, Vol. 42, No. 10, 1993, p. 1271-1278.

Research output: Contribution to journalArticle

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