Journal ArticleDOI
Scheduling multiprocessor job with resource and timing constraints using neural networks
Yueh-Min Huang,Ruey-Maw Chen +1 more
- Vol. 29, Iss: 4, pp 490-502
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TLDR
This work applies the Hopfield neural network and the normalized mean field annealing technique, respectively, to resolve a multiprocessor problem with no process migration, constrained times and limited resources.Abstract:
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.read more
Citations
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Journal ArticleDOI
A research survey: review of AI solution strategies of job shop scheduling problem
Banu Çaliş,Serol Bulkan +1 more
TL;DR: This paper focus on artificial intelligence approaches to NP-hard job shop scheduling (JSS) problem and successful approaches of artificial intelligence techniques such as neural network, genetic algorithm, multi agent systems, simulating annealing, bee colony optimization, ant colony optimization and particle swarm algorithm are presented.
Journal ArticleDOI
A review on evolution of production scheduling with neural networks
Derya Eren Akyol,G. Mirac Bayhan +1 more
TL;DR: A comprehensive overview on ANN approaches for solution of production scheduling problems is given, both theoretical developments and practical experiences are discussed, and research trends are identified.
Journal ArticleDOI
QoS provisioning dynamic connection-admission control for multimedia wireless networks using a Hopfield neural network
Chang Wook Ahn,R. S. Ramakrishna +1 more
TL;DR: Simulation results show that the algorithm can maximize resource utilization and maintain fairness in resource sharing, while maximizing the statistical multiplexing gain in providing acceptable service grades.
Journal ArticleDOI
Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system
TL;DR: Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving multiprocessor system scheduling problems with resource constraints.
Journal ArticleDOI
Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB
TL;DR: The simulation results reveal that the proposed approach in this investigation is novel and efficient for resource-constrained class scheduling problem.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Neurons with graded response have collective computational properties like those of two-state neurons.
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Book
Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
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