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Investigating Bayesian Optimization for rail network optimization
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DOI：10.1080/23248378.2019.1669500.png)
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摘要
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Optimizing the operation of rail networks using simulations is an on-going task where heuristic methods such as Genetic Algorithms have been applied. However, these simulations are often expensive to compute and consequently, because the optimization methods require many (typically >10(4)) repeat simulations, the computational cost of optimization is dominated by them. This paper examines Bayesian Optimization and benchmarks it against the Genetic Algorithm method. By applying both methods to test-tasks seeking to maximize passenger satisfaction by optimum resource allocation, it is experimentally determined that a Bayesian Optimization implementation finds 'good' solutions in an order of magnitude fewer simulations than a Genetic Algorithm. Similar improvement for real-world problems will allow the predictive power of detailed simulation models to be used for a wider range of network optimization tasks. To the best of the authors' knowledge, this paper documents the first application of Bayesian Optimization within the field of rail network optimization.
Keyword:
Bayesian Optimization
Genetic Algorithm
rail
network
optimization
simulation
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