求解VRP问题的混沌模拟退火萤火虫算法

发布时间：2018-04-23 00:32

本文选题：车辆路径问题 + 萤火虫优化算法　；参考：《包装工程》2017年07期

【摘要】：目的使萤火虫优化算法(GSO)能够适用于车辆路径问题(VRP)的求解,同时提高该算法的求解性能。方法通过对GSO算法的改进,提出求解VRP问题的混沌模拟退火萤火虫优化算法(CSAGSO)。首先,设计改进的GSO算法(IGSO)使IGSO算法能够适应VRP问题的求解;其次,在IGSO算法中引入模拟退火机制,提出模拟退火萤火虫优化算法(SAGSO),使IGSO算法可有效避免陷入局部极小并最终趋于全局最优。然后,在SAGSO算法中引入混沌机制,提出CSAGSO算法,对SAGSO算法的荧光素浓度值进行混沌初始化和混沌扰动;最后,对标准算例集进行仿真测试。结果与遗传算法、蚁群算法和粒子群算法相比,CSAGSO算法的全局寻优能力、收敛速度及稳定性均改善了50%以上。结论对GSO算法的改进是合理的,且CSAGSO算法的全局优化能力、收敛速度和稳定性均优于遗传算法、蚁群算法和粒子群算法。
[Abstract]:Aim to make the firefly optimization algorithm (GSO) suitable for solving the vehicle routing problem (VRP) and to improve the performance of the algorithm. Methods by improving the GSO algorithm, a chaotic simulated annealing firefly optimization algorithm for solving the VRP problem is proposed. Firstly, the improved GSO algorithm is designed so that the IGSO algorithm can adapt to the solution of the VRP problem. Secondly, the simulated annealing mechanism is introduced into the IGSO algorithm. A simulated annealing firefly optimization algorithm (SAGSOO) is proposed, which can effectively avoid falling into local minima and eventually be globally optimal. Then the chaotic mechanism is introduced into the SAGSO algorithm and the CSAGSO algorithm is proposed to initialize and perturb the concentration of fluorescein in the SAGSO algorithm. Finally the standard example set is simulated and tested. Results compared with genetic algorithm, ant colony algorithm and particle swarm optimization algorithm, the global optimization ability, convergence speed and stability of CSAGSO algorithm are improved by more than 50%. Conclusion the improvement of GSO algorithm is reasonable, and the global optimization ability, convergence speed and stability of CSAGSO algorithm are better than those of genetic algorithm, ant colony algorithm and particle swarm optimization algorithm.
【作者单位】：山西交通职业技术学院;
【分类号】：TP18

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