Convergence of an annealing algorithm
Mathematical Programming: Series A and B
ACM Transactions on Mathematical Software (TOMS)
Parallel simulated annealing techniques
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Performance of a new annealing schedule
DAC '88 Proceedings of the 25th ACM/IEEE Design Automation Conference
Simulated annealing with advanced adaptive neighborhood
Second international workshop on Intelligent systems design and application
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Hi-index | 0.00 |
A new way to implement the Simulated Annealing (SA) algorithm was developed and tested that improves computation performance by using shorter Markov chain length (inner iterations) and repeating the entire SA process until the final function value meets the solution criterion. The new approach coupled with the adaptive neighborhood method was tested on the Rosenbrock function in 4 and 13 dimensions. This implementation significantly improved the computation speed without degrading solution quality. The proposed implementation was used to characterize pulmonary architecture from micro CT image data demonstrating the algorithm's effectiveness especially for problems with high computational demand and when the solution quality requirement can be pre-specified. Using this implementation, detailed statistics of the morphometry of conducting airways from 12 male Sprague Dawley rats were obtained for each lobe.