A distributed guided genetic algorithm to solve the disturbance in the multimodal transport

The multimodal transport is a solution adopted by the governments to solve many challenges like the energy consumption and the pollution. Actually, the multimodal transport faces many problems as those related to the distribution, the focus of many researchers who have classified it as a NP-hard problem. The goal of this work is to develop a distributed guided genetic algorithm to solve the problem of multimodal transport, specially the disturbance. The solution must be valid in the normal case and in the degraded mode. So, this study aims to improve the quality of services offered to users. In fact, our approach is based on evolutionary algorithms, and more precisely on the genetic algorithm. We use hybridization in the selection operator and integration of a new structure in the mutation operator which supports on a multi-criteria method for the detection of itineraries.