Sensitive ant model for combinatorial optimization

Authors:
Camelia Chira;D. Dumitrescu;Camelia-Mihaela Pintea
Affiliations:
Babes-Bolyai University, Department of Computer Science, Cluj, Romania;Babes-Bolyai University, Department of Computer Science, Cluj, Romania;Babes-Bolyai University, Department of Computer Science, Cluj, Romania
Venue:
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Year:
2008

Citing 4
Cited 2

Swarm intelligence: from natural to artificial systems

Swarm intelligence: from natural to artificial systems
MAX-MIN Ant system

Future Generation Computer Systems
Self-Organization in Biological Systems

Self-Organization in Biological Systems
Ant colony optimization theory: a survey

Theoretical Computer Science

Image edge detection using ant colony optimization

WSEAS Transactions on Signal Processing
Evaluation of steady state vector of fuzzy autocatalytic set of fuzzy graph type-3 of an incineration process

WSEAS Transactions on Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

A combinatorial optimization metaheuristic called Sensitive Ant Model (SAM) based on the Ant Colony System (ACS) technique is proposed. ACS agents cooperate indirectly using pheromone trails. SAM improves and extends the ACS approach by enhancing each agent of the model with properties that induce heterogeneity. Agents are endowed with different pheromone sensitivity levels. Highly-sensitive agents are essentially influenced in the decision making process by stigmergic information and thus likely to select strong pheromone-marked moves. Search intensification can be therefore sustained. Agents with low sensitivity are biased towards random search inducing diversity for exploration of the environment. SAM is studied on a dynamic optimization problem. Numerical experiments offer promising results indicating the potential of the proposed SAM metaheuristic.