Application of Improved Ant Colony Algorithm

Hongyan Shi, Zhaoyu Bei
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引用次数: 59

Abstract

A stochastic optimization algorithm is proposed by combining ant colony (ACO) algorithm with artificial fish-swarm algorithm (AFSA) for solving continuous space optimization problems. The algorithm is improved with the rapid search capability of AFSA and the good search characteristics of ACO, and the convergence speed of the presented algorithm is also improved for avoiding being trapped in local optimization. The improved algorithm has been tested for varieties of functions. And the algorithm can handle these optimization problems very well.
改进蚁群算法的应用
将蚁群算法(ACO)与人工鱼群算法(AFSA)相结合,提出一种求解连续空间优化问题的随机优化算法。利用蚁群算法的快速搜索能力和蚁群算法良好的搜索特性对算法进行了改进,并提高了算法的收敛速度,避免了陷入局部寻优。改进后的算法已对各种函数进行了测试。该算法可以很好地处理这些优化问题。
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