Inspiration for optimization from social insect behaviour
By Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Nature, 2000
999 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Genetics
View Paper: https://www.semanticscholar.org/paper/df36fc99ca7277186c89cd5b5153cbf48e49111a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Victor O. K. Li
- Luca Maria Gambardella
- Christian Blum
- Holger R. Maier
- Albert Y. S. Lam
- Q. Henry Wu
- Aaron C. Zecchin
- Jon R. Saunders
In Computer Networks and Communications
- Christian Blum
- Dirk Helbing
- Albert Y. S. Lam
- Victor O. K. Li
- Luca Maria Gambardella
- D. Naug
- Xin-She Yang
- Marco Dorigo
In Genetics
Citations in the corpus
- Ant colony optimization: Introduction and recent trends (2005, background): “Seen from the artificial intelligence (AI) perspective, ACO algorithms are one of the most successful strands of swarm intelligence [16,17].”
- Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “Among the most famous ones are Simulated Annealing (SA) [4], Genetic Algorithm (GA) [5]-[7], and Ant Colony Optimization (ACO) [8], [ 9 ].”
- Computational models of collective behavior. (2005, background): “12 Bonabeau, E. et al. (2000) Inspiration for optimization from social”
- Structure and resilience of the social network in an insect colony as a function of colony size (2009, background): “The mechanisms by which the numerous colony members divide labor, partition tasks, and exchange information have attracted a great deal of interest not only from students of social insects but also from scientists hoping to apply such principles to engineering and computational problems (Bonabeau et”
- Ant Colony Optimization for Design of Water Distribution Systems (2003, background): “By maintaining pheromone trails and continuously exploring different options, ants serendipitously set up backup plans and are therefore prepared to respond to changes in their environment (Bonabeau and Theraulaz 2000).”
- Optimal traffic organization in ants under crowded conditions (2004, background): “, which have inspired engineers to design optimized artificial system”
- Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, background): “The first one is ant colony optimization (ACO), which was developed based on ants’ foraging behavior [3].”
- Ant Colony System for a Dynamic Vehicle Routing Problem (2005, background): “Customers appear during the working day with orders ranging in [1, 31].”