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首页> 外文期刊>International journal of knowledge-based and intelligent engineering systems >A novel approach to avoiding early stagnation in Ant Colony Optimization algorithms
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A novel approach to avoiding early stagnation in Ant Colony Optimization algorithms

机译：避免蚁群优化算法早期停滞的新方法

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摘要

This paper introduces a novel approach to avoiding early stagnation in Ant Colony Optimization algorithms. The approach involves oscillating the α and β parameters out of phase with each other according to an offline adaptation formula triggered by the online signal of stagnation in improved solutions across iterations. Further, in this paper, we present the experimental results obtained from applying this method to solving the Traveling Salesman Problem across eight fully connected, symmetric maps of sizes ranging from 51 to 1,400 cities, and show that a marginal improvement is achieved even with relatively constrained amounts of computation time and in the absence of fine-tuning of the ACO parameters towards each specific instance of the problem.
机译：本文介绍了一种避免蚁群优化算法中早期停滞的新颖方法。该方法涉及根据由迭代中的在线停滞信号触发的离线自适应公式，根据迭代中的改进解，使α和β参数彼此异相振荡。此外，在本文中，我们介绍了通过将这种方法应用于在51个到1400个城市范围内的八个完全连通的对称地图上求解旅行商问题而获得的实验结果，并且表明即使在相对受限的情况下也可以实现边际改进。大量的计算时间，并且没有针对问题的每个特定实例对ACO参数进行微调。

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