基于改进人工蜂群算法的货车载货平衡问题研究

Research on Truck Load Balancing Problem Based on Improved Artificial Bee Colony Algorithm

货车载货平衡问题是多约束的双目标规划问题,传统的规划算法往往具有较大的时间消耗。为此,针对ABC算法的特点,借鉴传统遗传算法求解组合优化问题上的优势、粒子群算法的历史极值与全局极值搜索机制及模拟退火的全局搜索策略,提出了一种求解该问题的新方法。在此基础上通过数值实验求解车厢个数为10、箱子个数为50的较复杂货车载货问题,并与传统的遗传算法进行对比,最终的结果表明改进算法对于货车载货平衡问题具有较高的求解效率。

The problem of truck load balance is a bi-objective programming with multi-conditions. The traditional planning method spends a lot of time. A new method is given to solve this problem in this paper,it based on the traditional legend method and characteristics of ABC algorithm with the advantages of combinatorial optimization,the historical extremum of swarm optimization algorithm,global extremum searching mechanism and global search strategy on simulated annealing. Finally,it uses numerical experiments to solve truck load balance problem with 10 compartments and 50 cases. Comparing with the traditional method,the improved algorithm has a high efficiency for the truck load balance problem.