An improved hybrid ant colony algorithm and its application in solving TSP

He Min, Pang Dazhi, Yang Song
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引用次数: 9

Abstract

Ant colony algorithm is a simulated evolutionary algorithm with the characteristics of positive feedback and distributed computation. It simulate the process of ants foraging to search the optimal solution. But the algorithm fall into local optimum easily and the convergence speed is very slow. After analyzing the disadvantages of ant colony algorithm, we put forward an improved hybrid ant colony algorithm. For each generation of ant colony perform crossover and mutation operations, and accept new individuals with a specified probability according to the Metropolis criterion of simulation annealing algorithm. Through series of simulation experiments' results, it can be found that the proposed algorithm is good at stability and optimization capacity.
一种改进的混合蚁群算法及其在求解TSP中的应用
蚁群算法是一种模拟进化算法,具有正反馈和分布式计算的特点。它模拟蚂蚁觅食寻找最优解的过程。但该算法容易陷入局部最优,且收敛速度很慢。在分析了蚁群算法的缺点后,提出了一种改进的混合蚁群算法。对每一代蚁群进行交叉和变异操作,并根据模拟退火算法的Metropolis准则以指定的概率接受新个体。通过一系列的仿真实验结果,可以发现该算法具有良好的稳定性和优化能力。
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