Modelling the Social Interactions in Ant Colony Optimization

Nishant Gurrapadi; Lydia Taw; Mariana Macedo; Marcos Oliveira; Diego Pinheiro; Carmelo Bastos-Filho; Ronaldo Menezes

doi:10.1007/978-3-030-33617-2_23

Modelling the Social Interactions in Ant Colony Optimization

Nishant Gurrapadi
, Lydia Taw
, Mariana Macedo^*
, Marcos Oliveira
, Diego Pinheiro
, Carmelo Bastos-Filho
, Ronaldo Menezes

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Ant Colony Optimization (ACO) is a swarm-based algorithm inspired by the foraging behavior of ants. Despite its success, the efficiency of ACO has depended on the appropriate choice of parameters, requiring deep knowledge of the algorithm. A true understanding of ACO is linked to the (social) interactions between the agents given that it is through the interactions that the ants are able to explore-exploit the search space. We propose to study the social interactions that take place as artificial agents explore the search space and communicate using stigmergy. We argue that this study bring insights to the way ACO works. The interaction network that we model out of the social interactions reveals nuances of the algorithm that are otherwise hard to notice. Examples include the ability to see whether certain agents are more influential than others, the structure of communication, to name a few. We argue that our interaction-network approach may lead to a unified way of seeing swarm systems and in the case of ACO, remove part of the reliance on experts for parameter choice.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings
Editors	Hujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Publisher	Springer
Pages	216-224
Number of pages	9
ISBN (Print)	9783030336165
DOIs	https://doi.org/10.1007/978-3-030-33617-2_23
Publication status	Published - 2019
Event	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - Manchester, United Kingdom Duration: 14 Nov 2019 → 16 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11872 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Country/Territory	United Kingdom
City	Manchester
Period	14/11/19 → 16/11/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Ant colony optimization
Interaction network
Social interactions
Swarm intelligence
Swarm-based algorithms

Access to Document

10.1007/978-3-030-33617-2_23

Persistent URL (handle)

Persistent link

Cite this

Gurrapadi, N., Taw, L., Macedo, M., Oliveira, M., Pinheiro, D., Bastos-Filho, C., & Menezes, R. (2019). Modelling the Social Interactions in Ant Colony Optimization. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings (pp. 216-224). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11872 LNCS). Springer. https://doi.org/10.1007/978-3-030-33617-2_23

Gurrapadi, Nishant ; Taw, Lydia ; Macedo, Mariana et al. / Modelling the Social Interactions in Ant Colony Optimization. Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. editor / Hujun Yin ; Richard Allmendinger ; David Camacho ; Peter Tino ; Antonio J. Tallón-Ballesteros ; Ronaldo Menezes. Springer, 2019. pp. 216-224 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Ant Colony Optimization (ACO) is a swarm-based algorithm inspired by the foraging behavior of ants. Despite its success, the efficiency of ACO has depended on the appropriate choice of parameters, requiring deep knowledge of the algorithm. A true understanding of ACO is linked to the (social) interactions between the agents given that it is through the interactions that the ants are able to explore-exploit the search space. We propose to study the social interactions that take place as artificial agents explore the search space and communicate using stigmergy. We argue that this study bring insights to the way ACO works. The interaction network that we model out of the social interactions reveals nuances of the algorithm that are otherwise hard to notice. Examples include the ability to see whether certain agents are more influential than others, the structure of communication, to name a few. We argue that our interaction-network approach may lead to a unified way of seeing swarm systems and in the case of ACO, remove part of the reliance on experts for parameter choice.",

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Gurrapadi, N, Taw, L, Macedo, M, Oliveira, M, Pinheiro, D, Bastos-Filho, C & Menezes, R 2019, Modelling the Social Interactions in Ant Colony Optimization. in H Yin, R Allmendinger, D Camacho, P Tino, AJ Tallón-Ballesteros & R Menezes (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11872 LNCS, Springer, pp. 216-224, 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, Manchester, United Kingdom, 14/11/19. https://doi.org/10.1007/978-3-030-33617-2_23

Modelling the Social Interactions in Ant Colony Optimization. / Gurrapadi, Nishant; Taw, Lydia; Macedo, Mariana et al.
Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. ed. / Hujun Yin; Richard Allmendinger; David Camacho; Peter Tino; Antonio J. Tallón-Ballesteros; Ronaldo Menezes. Springer, 2019. p. 216-224 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11872 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Taw, Lydia

AU - Macedo, Mariana

AU - Oliveira, Marcos

AU - Pinheiro, Diego

AU - Bastos-Filho, Carmelo

AU - Menezes, Ronaldo

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KW - Social interactions

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Gurrapadi N, Taw L, Macedo M, Oliveira M, Pinheiro D, Bastos-Filho C et al. Modelling the Social Interactions in Ant Colony Optimization. In Yin H, Allmendinger R, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Springer. 2019. p. 216-224. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33617-2_23

Modelling the Social Interactions in Ant Colony Optimization

Abstract

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Conference

Bibliographical note

Keywords

Access to Document

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