Impact of grafting a 2-opt algorithm based local searcher into the genetic algorithm
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Solving capacitated vehicle routing problems via genetic particle swarm optimization
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Solving capacitated vehicle routing problems by modified differential evolution
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
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To solve traveling salesman problems (TSP), a genetic differential evolution (GDE) was introduced, which was derived from the differential evolution (DE) and incorporated with the genetic reproduction mechanisms, namely crossover and mutation. The Greedy Subtour Crossover (GSX) was employed to generate an offspring to denote the difference of the parents. A modified ordered crossover (MOX) was employed to perform mutation to generate trial vector with a user defined parameter, the parameter were used to control the rates of the target vector components and the mutated vector components in the trial vector. Moreover, a 2-opt local search was implemented to enhance local search performance. GDE was implemented to the well-known TSP with 52, 100 and 200 cities with variable parameters. Based on analysis and discussion on the results, typical values of the parameters were given, with which GDE provided effective and robust performance.