SIAM Journal on Applied Mathematics
Cooling schedules for optimal annealing
Mathematics of Operations Research
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A note on the effect of neighborhood structure in simulated annealing
Computers and Operations Research
Simulation optimization using simulated annealing
Computers and Industrial Engineering
WSC '88 Proceedings of the 20th conference on Winter simulation
Simulation-based optimization using simulated annealing with confidence interval
WSC '04 Proceedings of the 36th conference on Winter simulation
New developments in ranking and selection: an empirical comparison of the three main approaches
WSC '05 Proceedings of the 37th conference on Winter simulation
Two simulated annealing algorithms for noisy objective functions
WSC '05 Proceedings of the 37th conference on Winter simulation
Structural and Multidisciplinary Optimization
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In many practical optimization problems, evaluation of a solution is subject to noise, e.g., due to stochastic simulations or measuring errors. Therefore, heuristics are needed that are capable of handling such noise. This paper first reviews the state-of-the-art in applying simulated annealing to noisy optimization problems. Then, two new algorithmic variants are proposed: an improved version of stochastic annealing that allows for arbitrary annealing schedules, and a new approach called simulated annealing in noisy environments (SANE). The latter integrates ideas from statistical sequential selection in order to reduce the number of samples required for making an acceptance decision with sufficient statistical confidence. Finally, SANE is shown to significantly outperform other state-of-the-art simulated annealing techniques on a stochastic travelling salesperson problem.