Multi-Objective Memetic Algorithm Based on NSGA-II and Simulated Annealing for Calibrating CORSIM Micro-Simulation Models of Vehicular Traffic Flow

被引：11

作者：

Cobos, Carlos ^{[1
]}

Erazo, Cristian ^{[1
]}

Luna, Julio ^{[1
]}

Mendoza, Martha ^{[1
]}

Gaviria, Carlos ^{[2
]}

Arteaga, Cristian ^{[2
]}

Paz, Alexander ^{[2
]}

机构：

[1] Univ Cauca, Popayan, Colombia

[2] Univ Nevada, Las Vegas, NV 89154 USA

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2016 | 2016年 / 9868卷

关键词：

Multi-objective optimization; NSGA-II; Memetic algorithm; Pareto front; Simulated annealing;

D O I：

10.1007/978-3-319-44636-3_44

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated with the proposed NSGA-II-SA whose performance is compared with two alternative state-of-the-art algorithms, a single-objective genetic algorithm which uses simulated annealing (GASA) and a simultaneous perturbation stochastic approximation algorithm (SPSA). The results illustrate the superiority of the NSGA-II-SA algorithm in terms of both runtime and convergence.

引用

页码：468 / 476

页数：9

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[2]

[Anonymous], 2010, ARXIV10040574

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