The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances
By Marco Dorigo, Thomas Stützle. 2003
972 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/ac08707894d1a33b3b22cde3ab5dbdcdcd0f208e
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
- Seyedali Mirjalili
- Abdolreza Hatamlou
- Marco Dorigo
- Thomas Stützle
- Kwang Mong Sim
- Weng Hong Sun
- Jean-Yves Potvin
- Michel Gendreau
In Computational Theory and Mathematics
- Seyedali Mirjalili
- Abdolreza Hatamlou
- Thomas Stützle
- Marco Dorigo
- Michel Gendreau
- Jean-Yves Potvin
- Ali Asghar Heidari
- Gaurav Dhiman
In Industrial and Manufacturing Engineering
- Seyedali Mirjalili
- Marco Dorigo
- Michel Gendreau
- Abdolreza Hatamlou
- Thomas Stützle
- Jean-Yves Potvin
- Kwang Mong Sim
- Weng Hong Sun
Citations in the corpus
- Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, methodology): “The ant colony optimization (ACO) algorithm is one of the first SI techniques mimicking the social intelligence of ants when foraging in an ant colony [8, 9].”
- Metaheuristics in Combinatorial Optimization (2005, background): “For a precise description of ACO and a historical perspective, see (Dorigo and Di Caro, 1999; Dorigo, Di Caro, and Gambardella, 1999; Dorigo and Gambardella, 1997; Dorigo, Maniezzo, and Colorni, 1996; Dorigo and Stützle, 2003; Maniezzo and Carbonaro, 2002; Taillard, 2002).”
- A short convergence proof for a class of ant colony optimization algorithms (2002, background): “10In practice, however, as shown by the results of many published experimental works (see [5], [6], [10] for an overview) this does not happen, and the bias introduced by the pheromone trails does indeed help to speed up convergence to an optimal solution.”
- Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, methodology): “PSO, GA, Ant Colony Optimization (ACO) [9, 10], Artificial Bee Colonies (ABC) [11], and GSA [11] are some of the most popular algorithms in this class.”
- Ant colony optimization for routing and load-balancing: survey and new directions (2003, background): “In the interesting and emerging field of ant colony optimization (ACO) [2], [4]–[7], a colony of (biological) ants is typically modeled as a society of mobile agents (or artificial ants).”