Ant Colony Optimization: Overview and Recent Advances
By Marco Dorigo, Thomas Stützle. Handbook of Metaheuristics, 2018
962 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/7e47f3e6972d9b7515d10f8cfbcd3b8f21e22b80
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
- Andreas Holzinger
- Vasile Palade
- Katharina Holzinger
- Gloria Cerasela Crisan
- Markus Plass
- Camelia-M. Pintea
- Akshay Ajagekar
- Fengqi You
In Industrial and Manufacturing Engineering
- Akshay Ajagekar
- Vasile Palade
- Andreas Holzinger
- Fengqi You
- Gloria Cerasela Crisan
- Katharina Holzinger
- Markus Plass
- Camelia-M. Pintea
Citations in the corpus
- Quantum computing for energy systems optimization: Challenges and opportunities (2019, methodology): “Meta-heuristic algorithms like simulated annealing [7], tabu search [8], genetic algorithms [9], ant-colony optimization [10] have grown quite popular due to their ability to tackle complex optimization problems where traditional methods prove ineffective.”
- Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach (2016, background): “To avoid the search getting stuck, typically before the pheromone trails get reinforced, all pheromone trails are decreased by a factor [30,31].”