Hidden Markov Model Classifier for the Adaptive ACS-TSP Pheromone Parameters

The Hidden Markov Models (HMM) are a powerful statistical techniques for modeling complex sequences of data. In this paper a Hidden Markov Model classifier is a special kind of these models that aims to find the posterior probability of each state given a sequence of observations and predicts the state with the highest probability. The purpose of this work is to enhance the performance of Ant Colony System algorithm applied to the Travelling Salesman Problem (ACS-TSP) by varying dynamically both local and global pheromone decay parameters based on the Hidden Markov Model algorithm, using two indicators: Diversity and Iteration that reflect the state of research space in a given moment. The proposed method was tested on several TSP benchmark instances, which compared with the basic ACS, the combination of Fuzzy Logic Controller (FLC) and ACS to prove the efficiency of its performance.

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