引用本文:李俊,周虎,李波.基于虚拟蚂蚁的局部优化蚁群算法[J].控制与决策,2019,34(11):2459-2468
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基于虚拟蚂蚁的局部优化蚁群算法
李俊,周虎
(武汉科技大学计算机科学与技术学院,武汉430065)
摘要:
蚁群算法在解决一些NPC(Non-deterministic polynomial complete)问题时具有较大的优势,但也存在一些不足,如收敛精度低、收敛速度慢等.为了平衡收敛精度与收敛速度之间的矛盾,提出一种基于虚拟蚂蚁的局部优化蚁群算法.该算法通过降低重复计算资源的比例来提高计算资源的利用率,从而提升较少迭代次数时的精度.对单位信息素和全局更新策略进行调整,使之与所提出的算法匹配.同时,增加两点局部优化算子——点交换和交叉去除,加快收敛速度,进一步提高解的精度.通过约束局部优化算子的参数,减少局部优化的计算量,使整体算法的复杂度与基本蚁群算法大致相当.从最终的实验数据可以得出,所提出的算法在较少迭代次数的情况下可以得出较高的精度,在收敛速度与收敛精度之间实现较好的平衡.
关键词:  虚拟蚂蚁  交叉去除  点交换  单位信息素  平衡矛盾  蚁群算法
DOI:10.13195/j.kzyjc.2018.0298
分类号:TP301.6;TP18
基金项目:国家自然科学基金项目(61572381);武汉科技大学智能信息处理与实时工业系统湖北省重点实验室基金项目(znxx2018QN06).
Local optimization ACO based on virtual ant colony algorithm
LI Jun,ZHOU Hu,LI Bo
(1. College of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan430065,China;2. Hubei Province Key Laboratory of Intelligent Infomation Processing and Real-time Industrial System,Wuhan 430065,China)
Abstract:
The ant colony algorithm has great advantages in solving some non-deterministic polynomial complete(NPC) problems, but there are some shortages, such as low convergence precision and slow convergence speed. In order to balance convergence accuracy and convergence speed, a local ant colony optimization algorithm based on virtual ants is proposed in this paper. This algorithm improves the efficiency of computing resources by reducing the proportion of repeated computing resources, thus improving the accuracy of less iterations. The unit pheromone and the global updating strategy are adjusted to make the method match. At the same time, the two-point local optimization operator-point exchange and cross removal are added to accelerate the convergence rate and further improve the precision of the solution. By constraining the parameters of local optimization operators , the computation of local optimization is reduced, and the complexity of the whole algorithm is approximately equal to that of the basic ant colony algorithm. From the final experimental data, it can be concluded that the algorithm can obtain higher precision with less iterative times, and a good balance is made between the convergence speed and the convergence precision.
Key words:  virtual ants  cross removal  point exchange  unit pheromone  equilibrium contradiction  ant colony algorithm

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