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Erschienen in:
Possibilistic Framework for Multi-objective Optimization Under Uncertainty Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Safae Bouzbita, Abdellatif El Afia, Rdouan Faizi

Erschienen in: Bioinspired Heuristics for Optimization

Verlag: Springer International Publishing

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Abstract

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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Vorheriges Kapitel Adapted Bin-Packing Algorithm for the Yard Optimization Problem
Nächstes Kapitel CHN and Min-Conflict Heuristic to Solve Scheduling Meeting Problems
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Metadaten
Titel
Hidden Markov Model Classifier for the Adaptive ACS-TSP Pheromone Parameters
verfasst von
Safae Bouzbita
Abdellatif El Afia
Rdouan Faizi
Copyright-Jahr
2019
Buch
Bioinspired Heuristics for Optimization

Print ISBN: 978-3-319-95103-4

Electronic ISBN: 978-3-319-95104-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-95104-1_10