Simulated annealing in the presence of noise
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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.
KeywordsSimulated annealing Uncertainty Noise Sequential sampling
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- Alkhamis, T.M., Ahmed, M.A.: Simulation-based optimization using simulated annealing with confidence interval. In: Ingalls, R.G. et al. (eds.) Winter Simulation Conference, pp. 514–519 (2004) Google Scholar
- Bowler, N.E.: The role of noise in optimisation and diffusion limited aggregation. Ph.D. thesis, University of Warwick (2001) Google Scholar
- Bulgak, A.A., Sanders, J.L.: Integrating a modified simulated annealing algorithm with the simulation of a manufacturing system to optimize buffer sizes in automatic assembly systems. In: Winter Simulation Conference, pp. 684–690. IEEE, New York (1988) Google Scholar
- Fink, T.M.A.: Inverse protein folding, hierarchical optimisation and tie knots. Ph.D. thesis, University of Cambridge (1998) Google Scholar
- Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley, Reading (1991) Google Scholar