Ant Colony Optimization Theory

By Marco Dorigo, Thomas Stützle. 2004

622 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/fec6403c682c0432e13c3f4d028da21dad28fec7

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. Muge Komurcu
2. Zikri Bayraktar
3. Han Yu
4. Zhiqi Shen
5. Chunyan Miao
6. Dusist Niyato
7. Douglas H. Werner
8. Cyril Leung

Citations in the corpus

• A survey: Ant Colony Optimization based recent research and implementation on several engineering domain (2012, background): “It can move from node i to node j. Dorigo and Stutzle (2004) redefined the pheromone update policy of ACO, and the term argentine ant is replaced with forward ant.”
• A Survey of Trust and Reputation Management Systems in Wireless Communications (2010, methodology): “It uses a modified ant colony (AC) algorithm [24] to accomplish this goal.”
• Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics (2010, methodology): “Nature-inspired techniques such as the Genetic Algorithm (GA) [1], Ant Colony Optimization (ACO) [2], and Particle Swarm Optimization (PSO) [3] have been shown to be some of the most effective global optimization strategies.”