混合遗传算法在物流配送路径优化上的应用

Application of Hybrid Genetic Algorithm on the Optimization of Logistics Distribution Path

文中关注物流配送路径优化问题,分析了遗传算法应用过程中数学模型的建立,并针对其局部搜索能力较弱的缺陷,将局部搜索能力较强的爬山算法与该算法相结合,从而构造出改进的混合遗传算法,用以解决物流配送的路径选择问题。通过实验进行计算,结果表明,文中设计的混合遗传算法在计算效率和算法稳定性方面都比标准遗传算法更优,有助于提升路径选择的合理性,以及确定最终的运输方案,从而降低运输成本,为解决物流配送路径优化问题提供了科学的决策依据。

This paper concerns the logistics distribution path optimization problem,and analyzes the establishment of mathematical model in the application of genetic algorithm.Aiming at the deficiency of genetic algorithm with low local search ability,combining genetic algorithm with hill-climbing algorithm with strong local search ability,then a hybrid genetic algorithm is constructed to solve the path selection problem of logistics distribution.Through experimental calculation,the results show that the designed hybrid genetic algorithm is superior to standard genetic algorithms in terms of computational efficiency and algorithm stability.It helps improve the rationality of path selection and determine the final transportation plan,so as to reduce transportation costs and provide a scientific decision-making basis for solving the problem of logistics distribution path optimization.