Improved Optimization Algorithm of Transit Route Network
Wenquan Li
Abstract
Wenquan Li
Abstract
In order to shorten residents’ travel time and reduce the operation cost of public transportation,the genetic algorithm is often adopted to optimize the transit network due to its global optimality. However,some drawbacks of this algorithm have received severe criticism. An improved genetic algorithm is proposed,which makes some progress in selection operator,crossover operator,mutation operator and the stop criterion of a simple genetic algorithm,and in transit route optimal choice of public transportation by decreasing the search space,adding optimal reserved and revised strategies. This proposed genetic algorithm could ensure the population diversity and accelerate its convergence; furthermore,it could avoid the phenomena of premature and slow the evolution obviously. A numerical simulation was then presented to demonstrate that this improved genetic algorithm is much more efficient than the simple one and highlights its potential applications in the optimization of transit network.
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In order to shorten residents’ travel time and reduce the operation cost of public transportation,the genetic algorithm is often adopted to optimize the transit network due to its global optimality. However,some drawbacks of this algorithm have received severe criticism. An improved genetic algorithm is proposed,which makes some progress in selection operator,crossover operator,mutation operator and the stop criterion of a simple genetic algorithm,and in transit route optimal choice of public transportation by decreasing the search space,adding optimal reserved and revised strategies. This proposed genetic algorithm could ensure the population diversity and accelerate its convergence; furthermore,it could avoid the phenomena of premature and slow the evolution obviously. A numerical simulation was then presented to demonstrate that this improved genetic algorithm is much more efficient than the simple one and highlights its potential applications in the optimization of transit network.
Key concepts: Crossover, Mathematical optimization, Genetic algorithm, Genetic operator, Public transport, Operator (biology), Computer science, Population