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针对基本蚁群算法在求解过程中容易出现收敛时间过长和陷入局部最优的不足,提出了一种动态自适应的蚁群算法(DSACO),在算法DSACO中改进了算法的重要参数,当算法疑似陷入局部最优时,通过自适应调整参数来提高全局最优解的求解质量和信息量强度;最后在煤炭运输问题上进行实验仿真,结果表明,DSACO算法与基本蚁群算法相比较,加快了收敛速度,提高了全局寻优能力。
Aiming at the shortcomings of the basic ant colony algorithm such as long time of convergence and falling into local optimum, a dynamic adaptive ant colony algorithm (DSACO) is proposed. In the algorithm DSACO, the important parameters of the algorithm are improved. When the algorithm is suspiciously submerged in local optima, the solution quality and the amount of information of the global optimal solution are improved by adaptively adjusting the parameters. Finally, an experimental simulation on the coal transport problem is carried out. The results show that compared with the basic ant colony algorithm, Speed up the convergence rate and improve the global optimization ability.