Full Text:   <2295>

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CLC number: U491; TP202

On-line Access: 2018-01-12

Received: 2016-07-08

Revision Accepted: 2016-10-27

Crosschecked: 2017-11-22

Cited: 0

Clicked: 6748

Citations:  Bibtex RefMan EndNote GB/T7714


Xi-qun Chen


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.11 P.1693-1704


A surrogate-based optimization algorithm for network design problems

Author(s):  Meng Li, Xi Lin, Xi-qun Chen

Affiliation(s):  Department of Civil Engineering, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   chenxiqun@zju.edu.cn

Key Words:  Network design problem, Surrogate-based optimization, Transportation planning, Heuristics

Meng Li, Xi Lin, Xi-qun Chen. A surrogate-based optimization algorithm for network design problems[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1693-1704.

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author="Meng Li, Xi Lin, Xi-qun Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T A surrogate-based optimization algorithm for network design problems
%A Meng Li
%A Xi Lin
%A Xi-qun Chen
%J Frontiers of Information Technology & Electronic Engineering
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%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601403

T1 - A surrogate-based optimization algorithm for network design problems
A1 - Meng Li
A1 - Xi Lin
A1 - Xi-qun Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 11
SP - 1693
EP - 1704
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601403

network design problems (NDPs) have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem (MNDP) more complicated and difficult to solve. We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuous, discrete, and mixed-integer). We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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