Adaptive simulated annealing (ASA): Lessons learned
By Lester Ingber. arXiv.org, 2000
667 citations recorded by the source corpus.
Adaptive s imulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more ef ficiently than by using other previous simulated annealing algorithms. The author’ sA SA code has been publicly available for ove rt wo y ears. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been used to further de velop the code. Some lessons learned, in particular some which are rele vant to other simulated annealing algorithms, are described.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/decd6a0c2ddf81bc765dd0bb620c5e48abf293f6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Jay D. Martin
- Timothy W. Simpson
- John E. Renaud
- Shawn E. Gano
- Michael G. Henderson
- A. F. Vaz
- Sébastien Lambot
- Slawomir Zak
Citations in the corpus
- A global multilevel coordinate search procedure for estimating the unsaturated soil hydraulic properties (2002, methodology): “Pan and Wu [1999] coupled the heuristic simulated annealing [Ingber, 1996] algorithm with a fast local downhill simplex algorithm to estimate five parameters of the Mualem-van Genuchten equations.”
- Cassini plasma spectrometer thermal ion measurements in Saturn's inner magnetosphere (2008, methodology): “[15] The minimization of the cost function is performed sequentially using three different optimizers, Adaptive Simulated Annealing (ASA) [Ingber, 1993a, 1993b, 1996], Simplex [Press et al., 1992] and Praxis [Brent, 1973; Powell, 1964] routines, in order to sample all parameter space to identify…”
- Firefly Algorithm for Continuous Constrained Optimization Tasks (2009, methodology): “The algorithm could be hybridized together with other heuristic local search based technique like Adaptive Simulated Annealing [13].”
- Update strategies for kriging models used in variable fidelity optimization (2006, methodology): “In this research, three different optimizers are used: Quasi-Newton, which is a gradient based local optimizer; pattern search (Kowalik and Osborne 1968), which is a local optimizer that doesn’t use gradient information; and adaptive simulated annealing (ASA; Ingber 1989, 1992, 1993, 1996 ), which ”
- A particle swarm pattern search method for bound constrained global optimization (2007, methodology): “One can clearly see from Table 1 that PSwarm required fewer function evaluations than DIRECT or ASA.”