Name
Abstract | ||
|---|---|---|
We present an application of Ant Colony Optimisation (ACO) to simulate socio-cognitive features of a population. We incorporated perspective taking ability to generate three different proportions of ant colonies: Control Sample, High Altercentricity Sample, and Low Alter-centricity Sample. We simulated their performances on the Travelling Salesman Problem and compared them with the classic ACO. Results show that all three ‘cognitively enabled’ ant colonies require less time than the classic ACO. Also, though the best solution is found by the classic ACO, the Control Sample finds almost as good a solution but much faster. This study is offered as an example to illustrate an easy way of defining inter-individual interactions based on stigmergic features of the environment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.procs.2015.05.234 | Procedia Computer Science |
Keywords | Field | DocType |
Ant Colony Optimization,Metaheuristics,Psychology,Cognition | Ant colony optimization algorithms,Population,Mathematical optimization,Computer science,Travelling salesman problem,Artificial intelligence,Socio-cognitive,Ant colony,Metaheuristic | Conference |
Volume | ISSN | Citations |
51 | 1877-0509 | 6 |
PageRank | References | Authors |
0.52 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mateusz Sekara | 1 | 6 | 0.52 |
| Michal Kowalski | 2 | 6 | 0.52 |
| Aleksander Byrski | 3 | 269 | 45.03 |
| Bipin Indurkhya | 4 | 193 | 51.14 |
| Marek Kisiel-Dorohinicki | 5 | 274 | 42.43 |
| Dana Samson | 6 | 18 | 3.01 |
| Tom Lenaerts | 7 | 276 | 53.44 |