基于信息素的自适应连续域混合蚁群算法

Pheromone based adaptive hybrid ant colony optimization for continuous domains

针对连续域混合蚁群算法(HACO)易陷入局部最优和收敛速度较慢的问题,提出了基于信息素的自适应连续域混合蚁群算法(QAHACO)。首先提出了一种新的解更新方式,对档案中的解进行信息素挥发,扩大了搜索范围,提高了算法的全局搜索能力,并且自适应地调整信息素挥发速率,更好地平衡收敛速度和收敛精度,其次采用了一种信息分享机制,将当前解与其他所有解的平均距离和当前解与至今最优解的距离相结合,进一步加快收敛速度。通过对测试函数进行仿真实验,结果表明,和连续域蚁群及其改进算法相比,QAHACO算法的寻优能力明显提高,寻优速度有一定的优势。

The Hybrid Ant Colony Optimization for continuous domains(HACO)easily traps into local optimum solutions and converges slowly, so Pheromone based Adaptive Hybrid Ant Colony Optimization for continuous domain algorithm(QAHACO)is put forward to solve these problems. Firstly, a new approach is proposed to update the solutions, which makes solutions pheromone itself evaporate, broaden search range and improve the global search ability. The introduction of the adaptive pheromone evaporation rate reaches a better balance between convergence speed and convergence accuracy.Secondly, an information sharing mechanism is proposed, combining the average distance between the chosen solution and all other solutions and the distance between the chosen solution and the optimal solution found, further improves the convergence speed. Through simulation on test function, the results show that, compared with ant colony optimization for continuous domains and its improved algorithm, the accuracy of QAHACO algorithm is improved significantly, and convergence speed of QAHACO algorithm has certain advantages.