.png){kind=icon}
返回
A search acceleration method for optimization problems with transport simulation constraints
.png){kind=icon}
OA
AI
DOI：10.1016/j.trb.2016.12.009.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work contributes to the rapid approximation of solutions to optimization problems that are constrained by iteratively solved transport simulations. Given an objective function, a set of candidate decision variables and a black-box transport simulation that is solved by iteratively attaining a (deterministic or stochastic) equilibrium, the proposed method approximates the best decision variable out of the candidate set without having to run the transport simulation to convergence for every single candidate decision variable. This method can be inserted into a broad class of optimization algorithms or search heuristics that implement the following logic: (i) Create variations of a given, currently best decision variable, (ii) identify one out of these variations as the new currently best decision variable, and (iii) iterate steps (i) and (ii) until no further improvement can be attained. A probabilistic and an asymptotic performance bound are established and exploited in the formulation of an efficient heuristic that is tailored towards tight computational budgets. The efficiency of the method is substantiated through a comprehensive simulation study with a non-trivial road pricing problem. The method is compatible with a broad range of simulators and requires minimal parametrization. (C) 2016 The Author. Published by Elsevier Ltd.
Keyword:
DAY-TO-DAY
STOCHASTIC EQUILIBRIUM ASSIGNMENT
APPROXIMATION
CALIBRATION
STABILITY
ALGORITHM
SELECTION
MODELS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
3.6K
被引数:
1.9W
机构
引用论文
分享收藏分享收藏A Computationally Efficient Simulation-Based Optimization Algorithm for Large-Scale Urban Transportation Problems大规模城市交通问题的计算效率高的基于仿真的优化算法
PREAI
分享收藏分享收藏Optimization by direct search: New perspectives on some classical and modern methods.png)
SIAM REVIEWOAAI
SIAM REVIEWIF6.1
OAAI分享收藏
查看更多内容