基于云模型的参数自适应蚁群遗传算法

ACGA with adapting parameters based on cloud models

蚁群算法基于正反馈机制进行全局搜索,具有很强的全局收敛能力;遗传算法具有极强的快速全局搜索能力。为了充分发挥两种算法在寻优过程中的优势,提出一种基于正态云关联规则的自适应参数调节蚁群遗传算法。该算法利用云关联规则实现了蚁群策略和遗传策略的有效融合,极大程度地发挥其整体功能,动态地平衡了算法收敛速度和搜索范围之间的矛盾,最后通过实例证明了其在解决TSP问题时的有效性。

The ant colony algorithm （ACA） has a good global convergence capability by using the mecha nism of positive feedback, while genetic algorithm （GA） has a capacity for performing global searches and being quick. CACGA （ant colonygenetic algorithm with adapting parameters based on cloud models） is proposed to take advantage of good qualities of the two optimization algorithms more completely. CBACGA makes the ant colony strategy and the genetic strategy to be fused ingeniously through the cloud association rule, which can u tilize the whole function of the algorithm effectively and can dynamically appease the contradiction between the convergent speed and the searching scope. The simulation result for TSP shows its validity.