Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging
A mathematical model of garden ants (Laius japonicus) is introduced herein to investigate the relationship between the distribution of the degree of stochasticity in following pheromone trails and the group foraging efficiency. Numerical simulations of the model indicate that depending on the system...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Shiraishi, Masashi Takeuchi, Rito Nakagawa, Hiroyuki Nishimura, Shin I Awazu, Akinori Nishimori, Hiraku |
| description | A mathematical model of garden ants (Laius japonicus) is introduced herein to
investigate the relationship between the distribution of the degree of
stochasticity in following pheromone trails and the group foraging efficiency.
Numerical simulations of the model indicate that depending on the systematic
change of the feeding environment, the optimal distribution of stochasticity
shifts from a mixture of almost deterministic and mildly stochastic ants to a
contrasted mixture of almost deterministic ants and highly stochastic ants. In
addition, the interaction between the stochasticity and the pheromone path
regulates the dynamics of the foraging efficiency optimization. Stochasticity
could strengthen the collective efficiency when stochasticity to the
sensitivity of pheromone for ants is introduced in the model. |
| doi_str_mv | 10.48550/arxiv.1805.05598 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1805_05598</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1805_05598</sourcerecordid><originalsourceid>FETCH-LOGICAL-a678-dc452eaf756253535ac2d76464655cb95bbe33c3e99fc68335188ad119addfc3</originalsourceid><addsrcrecordid>eNotj71OwzAURr10QC0PwIRfIGkc5yb2RpVSQIrUod2jG_8USyGubKsib08o6Azf9ukcQp5YkVcCoNhi-Ha3nIkC8gJAigfysnc3E6Khp-TVJ8bklEsz7QzqSJG2fvTTTL2luylFmjw9XpP7wpEefMCLmy4bsrI4RvP4v2tyOrye2_esO759tLsuw7oRmVYVlAZtA3UJfAFVqZu6WgBQg4RhMJwrbqS0qhacAxMCNWMStbaKr8nz3-u9oL-GxSHM_W9Jfy_hP4JtQyY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging</title><source>arXiv.org</source><creator>Shiraishi, Masashi ; Takeuchi, Rito ; Nakagawa, Hiroyuki ; Nishimura, Shin I ; Awazu, Akinori ; Nishimori, Hiraku</creator><creatorcontrib>Shiraishi, Masashi ; Takeuchi, Rito ; Nakagawa, Hiroyuki ; Nishimura, Shin I ; Awazu, Akinori ; Nishimori, Hiraku</creatorcontrib><description>A mathematical model of garden ants (Laius japonicus) is introduced herein to
investigate the relationship between the distribution of the degree of
stochasticity in following pheromone trails and the group foraging efficiency.
Numerical simulations of the model indicate that depending on the systematic
change of the feeding environment, the optimal distribution of stochasticity
shifts from a mixture of almost deterministic and mildly stochastic ants to a
contrasted mixture of almost deterministic ants and highly stochastic ants. In
addition, the interaction between the stochasticity and the pheromone path
regulates the dynamics of the foraging efficiency optimization. Stochasticity
could strengthen the collective efficiency when stochasticity to the
sensitivity of pheromone for ants is introduced in the model.</description><identifier>DOI: 10.48550/arxiv.1805.05598</identifier><language>eng</language><subject>Physics - Adaptation and Self-Organizing Systems</subject><creationdate>2018-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1805.05598$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1805.05598$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Shiraishi, Masashi</creatorcontrib><creatorcontrib>Takeuchi, Rito</creatorcontrib><creatorcontrib>Nakagawa, Hiroyuki</creatorcontrib><creatorcontrib>Nishimura, Shin I</creatorcontrib><creatorcontrib>Awazu, Akinori</creatorcontrib><creatorcontrib>Nishimori, Hiraku</creatorcontrib><title>Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging</title><description>A mathematical model of garden ants (Laius japonicus) is introduced herein to
investigate the relationship between the distribution of the degree of
stochasticity in following pheromone trails and the group foraging efficiency.
Numerical simulations of the model indicate that depending on the systematic
change of the feeding environment, the optimal distribution of stochasticity
shifts from a mixture of almost deterministic and mildly stochastic ants to a
contrasted mixture of almost deterministic ants and highly stochastic ants. In
addition, the interaction between the stochasticity and the pheromone path
regulates the dynamics of the foraging efficiency optimization. Stochasticity
could strengthen the collective efficiency when stochasticity to the
sensitivity of pheromone for ants is introduced in the model.</description><subject>Physics - Adaptation and Self-Organizing Systems</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj71OwzAURr10QC0PwIRfIGkc5yb2RpVSQIrUod2jG_8USyGubKsib08o6Azf9ukcQp5YkVcCoNhi-Ha3nIkC8gJAigfysnc3E6Khp-TVJ8bklEsz7QzqSJG2fvTTTL2luylFmjw9XpP7wpEefMCLmy4bsrI4RvP4v2tyOrye2_esO759tLsuw7oRmVYVlAZtA3UJfAFVqZu6WgBQg4RhMJwrbqS0qhacAxMCNWMStbaKr8nz3-u9oL-GxSHM_W9Jfy_hP4JtQyY</recordid><startdate>20180515</startdate><enddate>20180515</enddate><creator>Shiraishi, Masashi</creator><creator>Takeuchi, Rito</creator><creator>Nakagawa, Hiroyuki</creator><creator>Nishimura, Shin I</creator><creator>Awazu, Akinori</creator><creator>Nishimori, Hiraku</creator><scope>ALA</scope><scope>GOX</scope></search><sort><creationdate>20180515</creationdate><title>Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging</title><author>Shiraishi, Masashi ; Takeuchi, Rito ; Nakagawa, Hiroyuki ; Nishimura, Shin I ; Awazu, Akinori ; Nishimori, Hiraku</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-dc452eaf756253535ac2d76464655cb95bbe33c3e99fc68335188ad119addfc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Physics - Adaptation and Self-Organizing Systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Shiraishi, Masashi</creatorcontrib><creatorcontrib>Takeuchi, Rito</creatorcontrib><creatorcontrib>Nakagawa, Hiroyuki</creatorcontrib><creatorcontrib>Nishimura, Shin I</creatorcontrib><creatorcontrib>Awazu, Akinori</creatorcontrib><creatorcontrib>Nishimori, Hiraku</creatorcontrib><collection>arXiv Nonlinear Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shiraishi, Masashi</au><au>Takeuchi, Rito</au><au>Nakagawa, Hiroyuki</au><au>Nishimura, Shin I</au><au>Awazu, Akinori</au><au>Nishimori, Hiraku</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging</atitle><date>2018-05-15</date><risdate>2018</risdate><abstract>A mathematical model of garden ants (Laius japonicus) is introduced herein to
investigate the relationship between the distribution of the degree of
stochasticity in following pheromone trails and the group foraging efficiency.
Numerical simulations of the model indicate that depending on the systematic
change of the feeding environment, the optimal distribution of stochasticity
shifts from a mixture of almost deterministic and mildly stochastic ants to a
contrasted mixture of almost deterministic ants and highly stochastic ants. In
addition, the interaction between the stochasticity and the pheromone path
regulates the dynamics of the foraging efficiency optimization. Stochasticity
could strengthen the collective efficiency when stochasticity to the
sensitivity of pheromone for ants is introduced in the model.</abstract><doi>10.48550/arxiv.1805.05598</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.1805.05598 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_1805_05598 |
| source | arXiv.org |
| subjects | Physics - Adaptation and Self-Organizing Systems |
| title | Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T07%3A16%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Diverse%20Stochasticity%20Leads%20a%20Colony%20of%20Ants%20to%20Optimal%20Foraging&rft.au=Shiraishi,%20Masashi&rft.date=2018-05-15&rft_id=info:doi/10.48550/arxiv.1805.05598&rft_dat=%3Carxiv_GOX%3E1805_05598%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |