Solving the fuel transportation problem based on the improved genetic algorithm

Abstract

According to the characteristics of fuel transportation problem, the traditional genetic algorithm model is improved in this paper. The complexity of encoding is simplified by considering the condition of putting the distances of the tanker going halfway back and forth into the objective function. Scanning method is used to generate the initial population improving the quality of chromosomes in the initial population. Adopting the way of "interval crossover, random replacement" ensures the effectiveness and randomness of the crossover. Adding the operation of evolutionary cycle after crossover and mutation operation enhances the local search ability of the algorithm. Finally through MATLAB programming, the traditional genetic algorithm, the scanning genetic algorithm and the evolutionary cycle genetic algorithm and the improved genetic algorithm are compared which further verifies that the improved genetic algorithm is effective.