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Assessing Ant Colony Optimization Using Adapted Networks Science Metrics

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Hybrid Intelligent Systems (HIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 923))

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Abstract

This paper presents a method to assess the state of convergence of Ant Colony Optimization algorithms (ACO) using network science metrics. ACO are inspired by the behavior of ants in nature, and it is commonly used to solve combinatorial optimization problems. Network Science allows studying the structure and the dynamics of networks. This area of study provides metrics used to extract global information from networks in a particular moment. This paper aims to show that two network science metrics, the Clustering coefficient and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO. We analyze the convergence of the four variations of the ACO in the Traveling Salesman Problem (TSP). Based on the obtained results, we demonstrate that it is possible to evaluate the convergence of the ACO for the TSP based on the proposed metrics, especially the adapted clustering coefficient.

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Authors and Affiliations

  1. Polytechnic University of Pernambuco, University of Pernambuco, Recife, Brazil

    Sergio F. Ribeiro & Carmelo J. A. Bastos-Filho

Authors
  1. Sergio F. Ribeiro
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  2. Carmelo J. A. Bastos-Filho
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Corresponding author

Correspondence to Carmelo J. A. Bastos-Filho .

Editor information

Editors and Affiliations

  1. School of Engineering, Instituto Superior de Engenharia (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Department of Production and Systems, University of Minho, Guimarães, Portugal

    Maria Leonilde Varela

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Ribeiro, S.F., Bastos-Filho, C.J.A. (2020). Assessing Ant Colony Optimization Using Adapted Networks Science Metrics. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_13

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