1986
DOI: 10.1007/bf01582166
|View full text |Cite
|
Sign up to set email alerts
|

Convergence of an annealing algorithm

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
180
0
10

Year Published

1995
1995
2013
2013

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 596 publications
(190 citation statements)
references
References 7 publications
0
180
0
10
Order By: Relevance
“…SA is capable of surpassing local optima at high-medium temperatures and gradually converges as the temperature falls to zero. Asymptotically SA algorithm finds an optimal solution with probability one [25]; fortunately, a finite-time implementation of the algorithm returns near-optimal solutions for most problem instances [26]. The SA method requires calibrating the initial temperature, the length of the Markov chains and the cooling coefficient.…”
Section: Hybrid Simulated Annealing Algorithm With Mutation Operatormentioning
confidence: 99%
“…SA is capable of surpassing local optima at high-medium temperatures and gradually converges as the temperature falls to zero. Asymptotically SA algorithm finds an optimal solution with probability one [25]; fortunately, a finite-time implementation of the algorithm returns near-optimal solutions for most problem instances [26]. The SA method requires calibrating the initial temperature, the length of the Markov chains and the cooling coefficient.…”
Section: Hybrid Simulated Annealing Algorithm With Mutation Operatormentioning
confidence: 99%
“…Between 1984 and 1986, several authors independently proved that it is possible to design a simulated annealing algorithm so that the probability to be in a state with the minimum cost approaches one as the temperature approaches zero [123,124,125,126,127]. This property is called convergence.…”
Section: Convergence To Optimummentioning
confidence: 99%
“…The stationary probability distribution for our instance of annealing can be derived from the result of Lundy and Mees [126]. They proved a formula for the stationary probability distribution given that a number of requirements are satisfied.…”
Section: Chapter 7 Simulated Annealing At And/invertermentioning
confidence: 99%
“…Work on SA has looked at the theory (Hajek, 1988), cooling schedules (Lundy and Mees, 1986), and applications of SA such as sequencing (Osman and Potts, 1989;Ogbu and Smith, 1990), timetabling (Abramson, 1991) and the Steiner problem in graphs (Downsland, 1991). Further information on SA can be found in a variety of sources (Collins et al, 1988;Van Laarhoven and Aarts, 1989;Aarts and Korst, 1989).…”
Section: Equationmentioning
confidence: 99%