Algorithm code for "Collaborative optimization of last-train timetables for metro network to increase service time for passengers"

Published: 20 June 2022| Version 1 | DOI: 10.17632/sz9v8pv7tn.1
Contributor:
Fangsheng Wang

Description

Algorithm code for "Collaborative optimization of last-train timetables for metro network to increase service time for passengers"

Files

Steps to reproduce

1.Initialization: Initialize the first generation, and set parameters 2.Update parameter XXX according to Equations (42–43). 3.Calculate fitness values Calculate the fitness values of each chromosome with Equations (12–13) in Model 1, Equations (12, 16–21) in Model 2, and Equations (23–28) in Model 3. 4. Selection, crossover, mutation and health check. (4.1) Elite retention: The best chromosome is retained in the next generation. (4.2) Two selection strategies: the conventional parents,the differential parents. (4.3) The crossover and mutation operators, health check. 5. Evaluation. (5.1) Update the population complexity according to Equation (38). (5.2) Update the Q-values according to Equations (39–41). (5.3) Update the Boltzmann distribution according to Equations (42–43). 6. Termination conditions.

Institutions

Tongji University

Categories

Algorithms, Genetic Algorithm

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