Issue No. 10 - October (1993 vol. 42)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/12.257714
<p>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.</p>
Boolean neural network; traveling salesman problem; NP-complete problems; combinatorial optimization; simulation; simulated annealing algorithm; hardware complexity; noise immunity; real-time systems; job scheduling; Boolean functions; computational complexity; neural nets; real-time systems; scheduling; simulated annealing.
M. Kabuka, N. John and S. Bhide, "A Boolean Neural Network Approach for the Traveling Salesman Problem," in IEEE Transactions on Computers, vol. 42, no. , pp. 1271-1278, 1993.