TY - GEN
T1 - On the weak ergodicity of the markov chain associated with a chaotic simulated annealing algorithm
AU - Chen, Guo
AU - Dong, Zhao Yang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2008
Y1 - 2008
N2 - Chaotic simulated annealing (CSA) is a relatively new heuristic optimization technique and has been widely applied to optimization problems because of its simplicity and capability of finding fairly good solutions rapidly. However, currently only experimental results are used for verifying its superiority. In this paper, a new of chaotic simulated annealing method (CSA) is introduced and then a mathematic proof is given. It shows that the Markov Chain associated with the algorithm is weakly ergodic, which guarantees that the asymptotic behavior of the algorithm is independent of initial states. Furthermore, the theoretical analysis of the proposed CSA is very important to understand the essential features which make the algorithm work well. © 2008 IEEE.
AB - Chaotic simulated annealing (CSA) is a relatively new heuristic optimization technique and has been widely applied to optimization problems because of its simplicity and capability of finding fairly good solutions rapidly. However, currently only experimental results are used for verifying its superiority. In this paper, a new of chaotic simulated annealing method (CSA) is introduced and then a mathematic proof is given. It shows that the Markov Chain associated with the algorithm is weakly ergodic, which guarantees that the asymptotic behavior of the algorithm is independent of initial states. Furthermore, the theoretical analysis of the proposed CSA is very important to understand the essential features which make the algorithm work well. © 2008 IEEE.
UR - https://www.scopus.com/pages/publications/55749096217
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-55749096217&origin=recordpage
U2 - 10.1109/CEC.2008.4630937
DO - 10.1109/CEC.2008.4630937
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424418237
T3 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
SP - 1124
EP - 1127
BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
Y2 - 1 June 2008 through 6 June 2008
ER -