Improved Genetic Algorithm in Multi-objective Cargo Logistics Loading and Distribution

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 based improved genetic algorithm. This improves algorithm (P), that is, evolution mode draws lessons from coding algorithm, uses row insertion method obtain initial population. crossover operation, narrow gene similarity is used distinguish chromosome similarity, double variation rate added mutation operation process. The basic parameters are population size pop taken_ = 100, number iterations Max_ gen 200, selection probability 0.8, Local_ Pm 0.1 Global_ Pm=0.2 。 Matlab simulation calculate under different weight settings. When 1, shows stable downward trend after 30 generations converges 55 generations; However, convergence speed traditional very slow middle late stage, it does not begin converge until generation 126. basically has no fluctuation. From whole image, we can see two, connection between starting point point. slope significantly greater than fast, upward with increase iterations. Obviously, better