摘要
针对已有蚁群算法在复杂问题应用中寻优前期信息素匮乏、收敛速度慢的不足,通过引入信息权重因子和信息量均衡算子对蚁群的选择概率和信息素浓度进行自适应调节,提出了混合自适应蚁群算法。算例结果表明,该算法具有较快的寻优速度和更好的全局搜索能力,同时增加了解的多样性,减小了陷入局部极值的几率。
In order to solve the problem of pheromone shortage and slow convergent speed of existing ant system algorithm （AS） in its application to complex optimal searching, this paper presents a new hybrid adaptive ant system algorithm with pheromone weight multiplier and pheromone balance operator, which can adaptively adjust select probabilities and pheromone strength. The simulation results indicate that this algorithm is of a faster speed for optimum value searching and a better global optimal searching capability, and that at the same, the diversification of solutions is increased, and the probability falling into the local extreme values can be reduced.
出处
《西安理工大学学报》
CAS
2005年第4期405-408,共4页
Journal of Xi'an University of Technology
关键词
蚁群算法
混合自适应
权重因子
均衡算子
神经网络
ant system algorithm
hybrid self-adaptation
weight multiplier
balance operator
neural network
