Dynamic Real-Time Scheduling for Multi-Processor Tasks Using Genetic Algorithm
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
A portable and scalable algorithm for a class of constrained combinatorial optimization problems
Computers and Operations Research
Combining competitive scheme with slack neurons to solve real-time job scheduling problem
Expert Systems with Applications: An International Journal
A review on evolution of production scheduling with neural networks
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Fault-aware grid scheduling using performance prediction by workload modeling
The Journal of Supercomputing
A parallel solution for scheduling of real time applications on grid environments
Future Generation Computer Systems
Expert Systems with Applications: An International Journal
Multi-constraint system scheduling using dynamic and delay ant colony system
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Solving inequality constraints job scheduling problem by slack competitive neural scheme
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
A neural network realization of scheduling in grid computing environment
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Solving multiprocessor real-time system scheduling with enhanced competitive scheme
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Although providing rapid convergence to the solution, TSP frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal solution capable of preventing the local minimum. This important feature is embedded into a Hopfield neural network to derive a new technique, i.e., mean field annealing. This work applies the Hopfield neural network and the normalized mean field annealing technique, respectively, to resolve a multiprocessor problem (known to be a NP-hard problem) with no process migration, constrained times (execution time and deadline) and limited resources. Simulation results demonstrate that the derived energy function works effectively for this class of problems