带个体差异的蚁群算法的应用

Individual variation ant colony optimization algorithm and its application

基本蚁群算法在大规模优化问题的处理上,算法的执行效率很低。为此改进的算法引入了蚂蚁个体差异,并将不同蚂蚁选路策略混合应用,使改进后的蚁群算法在加快收敛速度和提高解的质量的同时,避免了过早停滞现象。实验表明,该算法在性能上远优于基本蚁群算法。

This paper presented an improvement on ant colony optimization （ACO） algorithm, introduced the individual variation in the ACO, which enabled the strategy of ants ＇ route selection to possess variety. Simulations show that the speed of convergence of the improved ACO algorithm can be enhanced greatly.