Green Logistics Vehicle Path Optimization Based on Hybrid Discrete Differential Evolution Algorithm

Abstract

Aiming at the problem of green logistics vehicle path optimization with the smallest carbon dioxide emissions, this paper proposes the Hybrid Discrete Differential Evolution (HDDE) algorithm. For the HDDE algorithm, adopt the integer-arranged encoding method and then encode the client as initial individual. Firstly, the mutation operation aims at reflecting the direction and degree of mutation preferably, by defining addition, subtraction and multiplication operation of the differential mutation operator based on permutation method, and simultaneously bringing in incremental subsequence position transformation; secondly, the method of partial cross mapping is to avoid the illegal offspring caused by the traditional single-point crossover; finally, combined with the 2-opt local search algorithm, the discrete differential evolution algorithm is prevented this operation from the local optimal solution so as to apply and solve the path optimization problem in discrete space all the times. Compared with the original discrete differential evolution algorithm and particle swarm optimization algorithm, the experimental results indicate: this algorithm has smaller minimum, average and standard deviation, which search for the minimum value as soon as possible and hardly become local optimum, as well as the better convergence performance which shows the algorithm will achieve a smaller carbon emission path and better effect of optimization in this paper.