Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/70214
Title: Evaluation of evolutionary algorithms for multi-objective train schedule optimization
Authors: Chang, C.S. 
Kwan, C.M.
Issue Date: 2004
Source: Chang, C.S.,Kwan, C.M. (2004). Evaluation of evolutionary algorithms for multi-objective train schedule optimization. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 3339 : 803-815. ScholarBank@NUS Repository.
Abstract: Evolutionary computation techniques have been used widely to solve various optimization and learning problems. This paper describes the application of evolutionary computation techniques to a real world complex train schedule multiobjective problem. Three established algorithms (Genetic Algorithm GA, Particle Swarm Optimization PSO, and Differential Evolution DE) were proposed to solve the scheduling problem. Comparative studies were done on various performance indices. Simulation results are presented which demonstrates that DE is the best approach for this scheduling problem. © Springer-Verlag Berlin Heidelberg 2004.
Source Title: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
URI: http://scholarbank.nus.edu.sg/handle/10635/70214
ISSN: 03029743
Appears in Collections:Staff Publications

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