基于隐马尔可夫模型的ACS-TSP局部信息素衰减参数的动态自适应

2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech) Pub Date : 2016-05-01 DOI:10.1109/CLOUDTECH.2016.7847719

Safae Bouzbita, A. El Afia, R. Faizi, Mustapha Zbakh

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引用次数: 15

摘要

本文的目的是提出一种基于隐马尔可夫模型的改进蚁群系统(ACS)算法，以动态适应局部信息素衰减参数ξ。该算法使用迭代和多样性作为ACS搜索空间中隐藏状态的指示器。为了测试算法的有效性，我们在几个旅行推销员问题(TSP)的基准实例上进行了实验。实验结果证明了该算法在收敛速度和解质量上的有效性。

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Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model

The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality.

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2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)

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