Most scheduling problems have been demonstrated to be NP-complete problems. The Hopfield neural network is commonly applied to obtain an optimal solution in various different scheduling applications, such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Hopfield neural networks, although providing rapid convergence to the solution, require extensive effort to determine coefficients. A competitive learning rule provides a highly effective means of attaining a sound solution and can reduce the effort of obtaining coefficients. Restated, the competitive mechanism reduces the network complexity. This important feature is applied to the Hopfield neural network to derive a new technique, i.e. the competitive Hopfield neural network technique. This investigation employs the competitive Hopfield neural network to resolve a multiprocessor problem with no process migration, time constraints (execution time and deadline), and limited resources. Simulation results demonstrate that the competitive Hopfield neural network imposed on the proposed energy function ensures an appropriate approach to solving this class of scheduling problems.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence