基于差分演化的自适应参数控制蚁群算法

蚁群算法存在对参数的依赖、早熟和停滞等缺点但具有与其他算法容易结合的特点，据此，将差分演化算法应用到蚁群算法的参数选取中，提出一种改进的蚁群算法。将蚁群算法的参数作为差分演化算法解空间的向量元素，在自适应地寻找蚁群算法最优参数组合的同时求解问题的最优解。改进算法对蚁群算法中的参数进行自适应调整，可避免大量盲目的测试，扩大蚁群算法的搜索空间，提高全局搜索能力。在典型的旅行商问题上进行对比实验，结果验证了改进算法的优化性能高于传统的蚁群算法。

Adaptive Parameter Control Ant Colony Algorithm Based on Differential Evolution

Aiming at the phenomena such as the dependence on parameter control,precocity and stagnation of Ant Colony Algorithm（ACA）,and the character that ACA is easily combined with other algorithms,the Differential Evolution（DE） algorithm is put into making decision of choosing the ACA＇s parameters.A new adaptive ACA is proposed,named DEAS.This algorithm regards the parameters of ACA as the elements of DE algorithm＇s solution vector and adaptively finds the optimal combination of parameters,and the optimal solution for solving the problem.The new algorithm effectively overcomes the influence of control parameters of ACA and decreases the numbers of useless experiments.It is adaptive,good at global-search and prevents the degradation of populations.The comparison with the basic ACA indicates DEAS improves the performance significantly.With some appropriate attempts the algorithm can also be used to solve other combinatorial optimization problems.