Document Type
Thesis
Date of Award
1-31-1991
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
Nirwan Ansari
Second Advisor
Edwin Hou
Third Advisor
Yun Q. Shi
Abstract
In this thesis, we develop an optimization method based on Mean Field Theory (MFT) Neural Networks to solve the Task Scheduling problem. The MFT algorithm combines characteristics of the Simulated Annealing (SA) algorithm and the Hopfield neural network. MFT exhibits rapid convergence and at the same time it preserves the solution quality afforded by SA. Since MFT has been successfully used to solve the Traveling Salesman Problem (TSP), a new modification to MFT is also presented which supports Task Scheduling problem. The temperature behavior of MFT during Task Scheduling is approximately analyzed and shown to possess a critical temperature (71,) at which most of the optimization occurs. This temperature is analogous to the gain of the neurons in a neural network and may be used to tune such networks for better performance.
Recommended Citation
Yi, Pei-Ken, "Efficient multiprocessor scheduling by mean field theory neural networks" (1991). Theses. 2682.
https://digitalcommons.njit.edu/theses/2682
