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
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.