ACO: Decisive Artificial Ants

Document Type : Original Article

Author

Abstract

Abstract
The Ant Colony Optimization was inspired by the foraging behavior of real ant colonies. The main determinants of an ant algorithm are the way of pheromone update and the transition probability of an ant‘s travel from a position to another. This paper proposes adopting the decision systems to develop the transition probability function and make other frequent decisions such as switching between ways of pheromone update. This increases the possibility of deriving and improving a variety of ant algorithms. The original transition probability function is investigated and other three formulas have been developed as general frameworks for the problems of multiple objective. The Analytic Hierarchy Process is followed as a base for this contribution; thus, unlimited number of factors can be involved. Furthermore, paradoxical views are discussed to synthesize different types of artificial stigmergy to energize the artificial ants with more robust interaction.

Keywords

Files

Share

How to cite

Statistics