Simulation-based optimization using simulated annealing with confidence interval

Authors:
Talal M. Alkhamis;Mohamed A. Ahmed
Affiliations:
Kuwait University, Safat, Kuwait;Kuwait University, Safat, Kuwait
Venue:
WSC '04 Proceedings of the 36th conference on Winter simulation
Year:
2004

Citing 3
Cited 4

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
A method for discrete stochastic optimization

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A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization

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Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems

Proceedings of the 38th conference on Winter simulation
Multi-objective simulation optimization through search heuristics and relational database analysis

Decision Support Systems
Simulated annealing in the presence of noise

Journal of Heuristics
A survey on metaheuristics for stochastic combinatorial optimization

Natural Computing: an international journal

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Abstract

This paper develops a variant of Simulated Annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulations. In the proposed variant of SA, the Metropolis criterion depends on whether the objective function values indicate statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, our method uses a constant temperature. We show that the configuration that has been visited most often in the first m iterations converges almost surely to a global optimizer.