STUDY ON OPTIMIZATION OF COAL LOGISTICS NETWORK BASED ON HYBRID GENETIC ALGORITHM

Coal is the main energy source in the world. The distribution and industrial layout of coal around the world are uneven, namely, production areas, reserve areas and consumption areas of coal are dislocated in space, so it is particularly important to have an excellent coal logistics network. Starting from the traditional genetic algorithm mechanism, aiming at the shortcomings of traditional genetic algorithm in solving problems of logistics transportation path optimization, such as precocity and insufficient local search ability, the paper proposes a hybrid genetic algorithm, combining partheno-genetic algorithm and traditional genetic algorithm in genetic manipulations and optimizes it based on the original genetics. This algorithm not only retains the optimization strategy of finding new and better individuals through genetic cross-mutation inheritance in traditional genetic algorithms, but also introduces the evolutionary function that can perform single gene transposition and is suitable for combinatorial optimization problems in partheno-genetic algorithms. Through mathematical models, simulation experiments are conducted on the basis of actual transportation network data. The experimental results show that compared with the original genetic algorithm and the simple partheno-genetic algorithm, the hybrid genetic algorithm improves the global optimization ability and the convergence speed of the algorithm. Therefore, it is proved that the hybrid genetic algorithm is more effective and has better applicability in terms of optimization of logistics distribution route.