Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Applied Optimization with MATLAB Programming
Applied Optimization with MATLAB Programming
| Hi-index | 0.00 |
This paper presents a stochastic traffic signal optimization method that consists of the CORSIM microscopic traffic simulation model and a heuristic optimizer. For the heuristic optimizer, the performance of three widely used optimization methods (i.e., genetic algorithm, simulated annealing and OptQuest Engine) was compared using a real world test corridor with 12 signalized intersections in Fairfax, Virginia, USA. The performance of the proposed stochastic optimization method was compared with an existing signal timing optimization program, SYNCHRO, under microscopic simulation environment. The results indicated that the genetic algorithm-based optimization method outperforms the SYNCHRO program as well as the other stochastic optimization methods in the optimization of traffic signal timings for the test corridor.