AN ANNEALING FRAMEWORK WITH LEARNING MEMORY

Authors
Citation
Cc. Lo et Cc. Hsu, AN ANNEALING FRAMEWORK WITH LEARNING MEMORY, IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, 28(5), 1998, pp. 648-661
Citations number
23
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Computer Science Cybernetics","Computer Science Theory & Methods","Computer Science Cybernetics","Computer Science Theory & Methods
ISSN journal
1083-4427
Volume
28
Issue
5
Year of publication
1998
Pages
648 - 661
Database
ISI
SICI code
1083-4427(1998)28:5<648:AAFWLM>2.0.ZU;2-7
Abstract
Simulated annealing can be viewed as a process that generates a sequen ce of Markov chains, i.e., it keeps no memory about the states visited in the past of the process. This property makes simulated annealing t ime-consuming in exploring needless states and difficult in controllin g the temperature and transition number, In this paper, we propose a n ew annealing model with memory that records important information abou t the states visited in the past. After mapping applications onto a ph ysical system containing particles with discrete states, the new annea ling method systematically explores the configuration space, learns th e energy information of it, and converges to a well-optimized state, S uch energy information is encoded in a learning scheme. The scheme gen erates states distributed in Boltzmann-style probability according to the energy information recorded in it. Moreover, with the assistance o f the learning scheme, controlling over the annealing process become s imple and deterministic. From qualitative and quantitative analyses in this paper, we can see that this convenient framework provides an eff icient technique for combinatorial optimization problems and good conf idence in the solution quality.