Cited By
View all- Hu JZhou EFan Q(2014)Model-Based Annealing Random Search with Stochastic AveragingACM Transactions on Modeling and Computer Simulation10.1145/264156524:4(1-23)Online publication date: 18-Nov-2014
Discrete optimization via approximate annealing adaptive search with stochastic averaging
Authors: 
Jiaqiao Hu, Chen WangAuthors Info & Claims
Jiaqiao Hu, Chen WangAuthors Info & ClaimsPages 4206 - 4216
Abstract
We propose a random search algorithm for black-box optimization with discrete decision variables. The algorithm is based on the recently introduced Model-based Annealing Random Search (MARS) for global optimization, which samples candidate solutions from a sequence of iteratively focusing distribution functions over the solution space. In contrast with MARS, which requires a sample size (number of candidate solutions) that grows at least polynomially with the number of iterations for convergence, our approach employs a stochastic averaging idea and uses only a small constant number of candidate solutions per iteration. We establish global convergence of the proposed algorithm and provide numerical examples to illustrate its performance.
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Index Terms
- Discrete optimization via approximate annealing adaptive search with stochastic averaging
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- IIE: Institute of Industrial Engineers
- INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
- SCS: Shanghai Computer Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
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Winter Simulation Conference
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Published: 11 December 2011
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WSC'11
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WSC '11 Paper Acceptance Rate 203 of 270 submissions, 75%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
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View all- Hu JZhou EFan Q(2014)Model-Based Annealing Random Search with Stochastic AveragingACM Transactions on Modeling and Computer Simulation10.1145/264156524:4(1-23)Online publication date: 18-Nov-2014
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