Multi-objective Memetic Algorithm Based on NSGA-II and Simulated Annealing for Calibrating CORSIM Micro-Simulation Models of Vehicular Traffic Flow
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- 关键词：Multi ; objective optimization ; NSGA ; II ; Memetic algorithm ; Pareto front ; Simulated annealing
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9868
- 期：1
- 页码：468-476
- 全文大小：1,426 KB
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13.Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, vol. 379. Springer, Heidelberg (2012) - 作者单位：Carlos Cobos (20)
Cristian Erazo (20)
Julio Luna (20)
Martha Mendoza (20)
Carlos Gaviria (21)
Cristian Arteaga (21)
Alexander Paz (21)
20. Universidad del Cauca, Popayán, Colombia
21. University of Nevada, Las Vegas, USA - 丛书名：Advances in Artificial Intelligence
- ISBN：978-3-319-44636-3
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
- 卷排序：9868
文摘
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.