Bio-inspired evolutionary dynamics on complex networks under uncertain cross-inhibitory signals
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 787039-1
- Type
- D - Journal article
- DOI
-
10.1016/j.automatica.2018.11.005
- Title of journal
- Automatica
- Article number
- -
- First page
- 61
- Volume
- 100
- Issue
- -
- ISSN
- 0005-1098
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2018
- URL
-
https://www.sciencedirect.com/science/article/pii/S0005109818305375
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The collective decision-making problem inspired by honeybee swarms has led to prominent development within the contexts of swarm robotics and social dynamics as exemplified by the work by Leonard and Couzin. The significance of this paper lies in treating the cross-inhibitory signals as time-varying parameters and most importantly in introducing an interaction topology via complex networks. This recent (2019) has paper directly influenced the direction of thought of collective decision-making by considering a structure within the model and has been further developed by other researchers such as (Tang et al., 2019) where they applied it to coupled neural networks.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
