Ant algorithms and stigmergy

By Marco Dorigo, Eric Bonabeau, Guy Theraulaz. Future generations computer systems, 2000

896 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f79e1ec55c4b0091487e9625b183d9a9379c9638

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

1. Thomas Stützle
2. Marco Dorigo
3. Christian Blum
4. Holger H. Hoos
5. Pinar Çivicioglu
6. Erkan Besdok
7. Ying Tan
8. Abbas Mohasel Afshar

In Genetics

1. Pinar Çivicioglu
2. Erkan Besdok
3. Thomas Stützle
4. Ying Tan
5. Marco Dorigo
6. Holger H. Hoos
7. Zhongyang Zheng

In Sociology and Political Science

1. Pinar Çivicioglu
2. Erkan Besdok
3. Thomas Stützle
4. Ying Tan
5. Marco Dorigo
6. Holger R. Maier

Citations in the corpus

• MAX-MIN Ant System (2000, background): “The (artificial) pheromone trails are a kind of distributed numeric information (called stigmergic information in [9]) which is modified by the ants to reflect their experience accumulated while solving a particular problem.”
• Ant Colony Optimization for Design of Water Distribution Systems (2003, methodology): “Ant colony optimization algorithms (ACOAs) have been successfully applied to a number of benchmark combinatorial optimization problems, such as the traveling salesman and quadratic assignment problems (Dorigo et al. 2000), and have been shown to outperform other evolutionary optimization algorithms,”
• Research Advance in Swarm Robotics (2013, background): “[42] Dorigo M, Bonabeau E, Theraulaz G.”
• Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation (2007, methodology): “More recently, successful applications of ACO algorithms to a number of engineering design and operation problems, in various fields, has been reported [9–12].”
• The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances (2003, background): “be/ ̃ants/) where researchers meet to discuss the properties of ACO and other ant algorithms [8, 9, 30], both theoretically and experimentally.”
• A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “The methods which predicate on the collective search of the patterns for the solutions of the related problem are generally known as the artificial swarm intelligence methods (Dorigo et al. 2000, 2004; Martinoli et al. 2004; Sousa et al. 2004; Mahamed and Mehrdad 2008; Karaboga and Akay 2009b).”
• Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “They distribute control through local rules between multiple agents, enabling the group to perform its tasks even when one or more individuals fail [46].”
• MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach (2001, background): “Ant colony optimization is one of the most successful models of swarm-based approaches for optimization, where the search algorithm is inspired by the behavior of real ants (Dorigo and Gambardella 1997; Dorigo and Caro 1999; Dorigo et al. 2000 ).”