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An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

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Abstract

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization.

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Notes

  1. http://www.ics.uci.edu/~mlearn/MLRepository.html.

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  1. Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Zheng Jun, Tan Yu-An & Zhang Xue-Lan

  2. School of Computers, National University of Defense Technology, Changsha, China

    Lu Jun

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  1. Zheng Jun
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Jun, Z., Yu-An, T., Xue-Lan, Z. et al. An improved dynamic structure-based neural networks determination approaches to simulation optimization problems. Neural Comput & Applic 19, 883–901 (2010). https://doi.org/10.1007/s00521-010-0348-x

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