Updating ACO Pheromones Using Stochastic Gradient Ascent and Cross-Entropy Methods

Authors:
Marco Dorigo;Mark Zlochin;Nicolas Meuleau;Mauro Birattari
Affiliations:
-;-;-;-
Venue:
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Year:
2002

Citing 5
Cited 7

The ant colony optimization meta-heuristic

New ideas in optimization
Ant colony optimization and stochastic gradient descent

Artificial Life
AntNet: distributed stigmergetic control for communications networks

Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

When Model Bias Is Stronger than Selection Pressure

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Model-Based Search for Combinatorial Optimization: A Comparative Study

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
ACO Applied to Group Shop Scheduling: A Case Study on Intensification and Diversification

ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Toward the Formal Foundation of Ant Programming

ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant colony optimization theory: a survey

Theoretical Computer Science
Ant Based Heuristics for the Capacitated Fixed Charge Location Problem

ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
A novel fault-tolerant execution model by using of mobile agents

Journal of Network and Computer Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we introduce two systematic approaches, based on the stochastic gradient ascent algorithm and the cross-entropy method, for deriving the pheromone update rules in the Ant colony optimization metaheuristic. We discuss the relationships between the two methods as well as connections to the update rules previously proposed in the literature.