| 摘要: |
| 目的 设计一种求解包装配送问题的混沌蛙跳布谷鸟算法（Chaotic Frog Leaping Cuckoo Search Algorithm, CFLCSA）。方法 对鸟巢个体进行实数编码，引入混沌机制和随机蛙跳算法，增强算法种群多样性和局部搜索能力，并利用E-n33-k4和E-n76-k8算例来验证算法的求解性能。结果 CFLCSA算法能够求得E-n33-k4已知最优解，求得E-n76-k8的最短配送距离与已知最优解的误差仅为5.03%，且算法求解结果及平均运行时间均优于混沌蚁群算法（Chaotic Ant Colony Algorithm, CACA）、改进遗传算法（Improved Genetic Algorithm, IGA）和禁忌搜索算法（Tabu Search, TS）。结论 CFLCSA算法求解性能优于CACA算法、IGA算法和TS算法，是一种较好的包装配送问题求解方法。 |
| 关键词: 包装配送 布谷鸟算法 混沌机制 蛙跳算法 |
| DOI：10.19554/j.cnki.1001-3563.2019.05.024 |
| 分类号:TP301 |
| 基金项目:重庆市教委人文社科项目（16SKGH209）；重庆市社科规划项目（2018YBGL071） |
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| Chaos Frog Leaping Cuckoo Search Algorithm in Packaging Distribution Problem |
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ZHANG Yi
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Chongqing Technology and Business Institute, Chongqing 401520, China
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| Abstract: |
| The work aims to design a chaotic frog leaping cuckoo search algorithm (CFLCSA) to solve packaging distribution problem. The nests were coded by real number and the chaotic mechanism and random leaping frog algorithm were introduced to enhance the population diversity and local search capability of CFLCSA. E-n33-k4 and E-n76-k8 were also used to verify the performance of CFLCSA. The known optimal solution of E-n33-k4 was obtained by CFLCSA algorithm, the error between the shortest distance of E-n76-k8 and the known optimal solution was only 5.03%, and solution results and mean running time of CFLCSA were better than Chaotic Ant Colony Algorithm (CACA), Improved Genetic Algorithm (IGA) and Tabu Search (TS) algorithm. CFLCSA algorithm is superior to CACA, IGA and TS algorithm in solving performance and is a better solution for packaging distribution problem. |
| Key words: packaging distribution cuckoo search algorithm chaotic mechanism leaping frog algorithm |
