Improved Genetic Algorithm in Multi-objective Cargo Logistics Loading and Distribution
DOI:
https://doi.org/10.31449/inf.v47i2.3958Abstract
In order to solve the problem of material distribution path planning in production workshop, this paper proposes a research on multi-objective cargo logistics loading and distribution based on improved genetic algorithm. This paper improves the genetic algorithm to solve the problem (P), that is, the evolution mode based on genetic algorithm draws lessons from the coding mode of genetic algorithm, and uses the row insertion method to obtain the initial population. In the crossover operation, the narrow gene similarity is used to distinguish the chromosome similarity, and the double variation rate is added to the mutation operation in the evolution process. The basic parameters of genetic algorithm are improved and the population size pop is taken_ size = 100, number of iterations Max_ gen = 200, selection probability = 0.8, crossover probability = 0.8, double mutation probability Local_ Pm = 0.1 and Global_ Pm=0.2。 Matlab simulation is used to calculate under different weight settings. When = 1, the improved genetic algorithm shows a stable downward trend after 30 generations and converges to 55 generations; However, the convergence speed of traditional genetic algorithm is very slow in the middle and late stage, and it does not begin to converge until generation 126. When = 1, the improved genetic algorithm basically has no fluctuation. From the whole image, we can see the downward trend of the two, that is, the connection between the starting point and the convergence point. The slope of the improved genetic algorithm is significantly greater than that of the traditional genetic algorithm. When = 1, the convergence speed of the improved genetic algorithm is fast, and it shows a stable upward trend with the increase of the number of iterations. Obviously, the improved genetic algorithm is better than the traditional genetic algorithm.
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