A Parallel Genetic Algorithm for Solving the Container Loading Problem
A Parallel Genetic Algorithm for Solving the Container Loading Problem
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DOI：
10.1111/1475-3995.00369
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发表时间：
2002-07
影响因子：
3.1
通讯作者：
H. Gehring;Andreas Bortfeldt
中科院分区：
文献类型：
--
作者：
H. Gehring;Andreas Bortfeldt
针对单集装箱装载问题，提出了一种并行遗传算法（PGA）。重点放在强异构负载的情况下。PGA遵循迁移模型。几个独立的亚种群经受着彼此独立的进化过程。同时，最优秀的个体在亚种群之间进行交换。不同子种群的演化在相应数量的局域网工作站上进行。PGA的质量通过广泛的比较测试证明，包括知名的参考问题和其他作者的加载程序。
This paper presents a parallel genetic algorithm (PGA) for the container loading problem with a single container to be loaded. The emphasis is on the case of a strongly heterogeneous load. The PGA follows a migration model. Several separate sub-populations are subjected to an evolutionary process independently of each other. At the same time the best individuals are exchanged between the sub-populations. The evolution of the different sub-populations is carried out on a corresponding number of LAN workstations. The quality of the PGA is demonstrated by an extensive comparative test including well-known reference problems and loading procedures from other authors.