ACM Transactions on Mathematical Software (TOMS)
Cooling schedules for optimal annealing
Mathematics of Operations Research
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Parallel simulated annealing by mixing of states
Journal of Computational Physics
Simulated annealing algorithms for continuous global optimization: convergence conditions
Journal of Optimization Theory and Applications
Convergence of the simulated annealing algorithm for continuous global optimization
Journal of Optimization Theory and Applications
Parallel Simulated Annealing Algorithms in Global Optimization
Journal of Global Optimization
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
The interacting-particle algorithm with dynamic heating and cooling
Journal of Global Optimization
Population-based simulated annealing for traveling tournaments
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new populatoin-based simulated annealing algorithm
Proceedings of the Winter Simulation Conference
Combining gradient-based optimization with stochastic search
Proceedings of the Winter Simulation Conference
Hi-index | 0.00 |
In this paper, we propose a population-based optimization algorithm, Sequential Monte Carlo Simulated Annealing (SMC-SA), for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing. We prove an upper bound on the difference between the empirical distribution yielded by SMC-SA and the Boltzmann distribution, which gives guidance on the choice of the temperature cooling schedule and the number of samples used at each iteration. We also prove that SMC-SA is more preferable than the multi-start simulated annealing method when the sample size is sufficiently large.