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Self-organizing nest migration dynamics synthesis for ant colony systems

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Abstract

In this study, we synthesize a novel dynamical approach for ant colonies enabling them to migrate to new nest sites in a self-organizing fashion. In other words, we realize ant colony migration as a self-organizing phenotype-level collective behavior. For this purpose, we first segment the edges of the graph of ants’ pathways. Then, each segment, attributed to its own pheromone profile, may host an ant. So, multiple ants may occupy an edge at the same time. Thanks to this segment-wise edge formulation, ants have more selection options in the course of their pathway determination, thereby increasing the diversity of their colony’s emergent behaviors. In light of the continuous pheromone dynamics of segments, each edge owns a spatio-temporal piece-wise continuous pheromone profile in which both deposit and evaporation processes are unified. The passive dynamics of the proposed migration mechanism is sufficiently rich so that an ant colony can migrate to the vicinity of a new nest site in a self-organizing manner without any external supervision. In particular, we perform extensive simulations to test our migration dynamics applied to a colony including 500 ants traversing a pathway graph comprising 200 nodes and 4000 edges which are segmented based on various resolutions. The obtained results exhibit the effectiveness of our strategy.

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References

  • Abbasy A, Hosseini SH (2007) Ant colony optimization-based approach to optimal reactive power dispatch: a comparison of various ant systems. In: 2007 IEEE power engineering society conference and exposition in Africa-PowerAfrica. IEEE, pp 1–8

  • Al-Shihabi ST, AlDurgam MM (2017) A max-min ant system for the finance-based scheduling problem. Comput Ind Eng 110:264–276

    Article  Google Scholar 

  • Ashraf A, Porres I (2018) Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int J Parallel Emergent Distrib Syst 33(1):103–120

    Article  Google Scholar 

  • Biswas PK, Phoha S (2006) Self-organizing sensor networks for integrated target surveillance. IEEE Trans Comput 55(8):1033–1047

    Article  Google Scholar 

  • Camazine S, Deneubourg J-L, Franks NR, Sneyd J, Theraula G, Bonabeau E (2020) Self-organization in biological systems. Princeton University Press, Princeton

    Book  Google Scholar 

  • Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7:20281–20292

    Article  Google Scholar 

  • Deng W, Xu J, Song Y, Zhao H (2020) An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application. Int J Bio-Inspir Comput 16(3):158–170

    Article  Google Scholar 

  • Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1(1):53–66

    Article  Google Scholar 

  • Hagberg A, Swart P, Chult DS (2008) Exploring network structure, dynamics, and function using NetworkX. In: Los Alamos National Lab.(LANL), Los Alamos, NM (United States), Tech. Rep

  • Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5(3):6915

    Article  Google Scholar 

  • Kydonieus AF (2019) Insect suppression with controlled release pheromone systems: volume I. CRC Press, Boca Raton

    Book  Google Scholar 

  • Leclerc J-B, Pinto Silva J, Detrain C (2018) Impact of soil contamination on the growth and shape of ant nests. R Soc Open Sci 5(7):180267

    Article  Google Scholar 

  • Li G, Boukhatem L (2013) Adaptive vehicular routing protocol based on ant colony optimization. In: Proceeding of the tenth ACM international workshop on vehicular inter-networking, systems, and applications, pp 95–98

  • Macktoobian M, Aliyari Sh M (2017) Optimal distributed interconnectivity of multi-robot systems by spatially-constrained clustering. Adapt Behav 25(2):96–113

    Article  Google Scholar 

  • Macktoobian M, Duc GF (2022) Meta navigation functions: adaptive associations for coordination of multi-agent systems. In: American control conference (ACC). IEEE, pp 1921–1926

  • Meng X-B, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):673–687

    Article  Google Scholar 

  • O’Shea-Wheller TA, Sendova-Franks AB, Franks NR (2016) Migration control: a distance compensation strategy in ants. Sci Nat 103(7):1–9

    Google Scholar 

  • Pitcher T (2001) Fish schooling. In: Encyclopedia of ocean sciences: marine biology, pp 337–349

  • Skaldina O, Peräniemi S, Sorvari J (2018) Ants and their nests as indicators for industrial heavy metal contamination. Environ Pollut 240:574–581

    Article  Google Scholar 

  • Türk S, Radeke R, Lehnert R (2010) Network migration using ant colony optimization. In: 2010 9th conference of telecommunication, media and internet. IEEE, pp 1–6

  • Wang Y, Geng C, Xu N (2021) Assembly sequence optimization based on hybrid symbiotic organisms search and ant colony optimization. Soft Comput 25(2):1447–1464

    Article  Google Scholar 

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  1. Electrical and Computer Engineering Department, University of Alberta, Edmonton, AB, Canada

    Matin Macktoobian

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  1. Matin Macktoobian
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Correspondence to Matin Macktoobian.

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Macktoobian, M. Self-organizing nest migration dynamics synthesis for ant colony systems. Nat Comput (2022). https://doi.org/10.1007/s11047-022-09923-0

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