基于改进多种群遗传算法的货位优化研究
首发时间:2020-01-21
摘要:面向立体仓库的货位优化研究对于提高运转效率,从而降低运作成本具有重要意义。针对这一问题,本文首先分析了货位优化的实际需求及目标,并对该问题进行建模;其次,本文对传统多种群遗传算法的排列编码方式进行改进,使该启发式算法其更能适用于搜索离散的多目标优化问题的近似解,并将该算法应用于货位优化问题。为对该算法进行性能评价,本文在多条件下对该算法和传统遗传算法分别进行了仿真实验。实验结果表明,面向货位优化问题,该算法较之传统遗传算法,稳定性更高,收敛速度更快,寻优结果更优。由此可见,该算法因其紧凑的基因编码方式,既能提高遗传算法的全局寻优能力,又能减少启发式算法搜索无意义空间引起的算力消耗,进而提高搜索效率。
关键词: 多种群遗传算法 货位优化问题 改进排列编码 货位优化建模 多目标优化算法
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Improved Multi-population Genetic Algorithm for Slotting Optimization
Abstract:The research on slotting optimization for three-dimensional warehouse is of great significance to improve operation efficiency and reduce operation cost accordingly. To this end, first, the actual needs and objectives of slotting optimization is analyzed and modelled. In addition, this paper improves the permutation coding method of traditional multi-population genetic algorithm, which makes this heuristic algorithm more suitable for searching the approximate solution of discrete multi-objective optimization problem. Moreover, such algorithm is leveraged to attack the slotting optimization problem. In order to evaluate the performance of the algorithm, the simulation experiments of the proposed algorithm and the traditional genetic algorithm are carried out under multiple conditions. The experimental results indicate that this algorithm is more stable and converges faster than the traditional genetic algorithm. It can be seen that the proposed method can not only improve the global optimization ability of genetic algorithm, but also reduce the computational power consumption caused by the meaningless searching when using heuristic algorithm, thus improving the search efficiency.
Keywords: Multi-population genetic algorithm slotting optimization improved permutation coding slotting optimization modelling multiobjective optimization algorithm
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