Ant Colony Optimization
Full Text(PDF, 3000) PP.
| Author(s) |
|Utkarsh Jaiswal, Shweta Aggarwal|
| KEYWORDS |
Metaheuristic, Pheromones, Resource-constrained project scheduling problem (RCPSP), Stigmergy, Swarm Intelligence
Ant colony optimization (ACO) is a new natural computation method from mimic the behaviors of ant colony. It is a very good combination optimization method. Ant colony optimization algorithm was recently proposed algorithm, it has strong robustness as well as good distributed calculative mechanism, and it is easy to combine with other methods, and the well performance has been shown on resolving the complex optimization problem. An ant colony optimization approach for the resource-constrained project scheduling problem (RCPSP) is presented. The TSP problem is chosen as example for introducing the basic principle of ACO, and several improvement algorithms are present for the problem of ACO.
 Ant colony optimization, Macro Dorigo, Thomas
 Ant Algorithms, Marco Dorigo, Gianni Di
Caro, Michael Sampels.
 Swarm Intelligence, Eric Bonabeau, Guy
Theraulaz, Marco Dorigo.
 Swarm Intelligence, Christian Blum, Daniel
 Swarm Intelligence, James Kennedy, Russell C.
Eberhart, Yuhui Shi.
 Ant Colony Optimization and Swarm Intelligence,
Dorigo, M.; Birattari, M.; Blum, C.; Gambardella,
L.M.; Mondada, F.; Stützle, Th. (Eds.)
 Bio Inspired Artificial Intelligence, Dario
Floreano, Claudio Mattiussi.
 Artificial Intelligence-A modern approach, Staurt
Russell and Peter Norvig.
 Artificial Intelligence- A system approach, M.
 Introducing Artificial Intelligence, Henry
 The Essence of Artificial Intelligence, Alison
 Artificial Intelligence, Patrick Henry Winston.
 Artificial Intelligence, Elain Rich and Kevin