Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Modeling the dynamics of ant colony optimization
Evolutionary Computation
Ant Colony Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
A new ant colony algorithm for multi-label classification with applications in bioinfomatics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Runtime analysis of a simple ant colony optimization algorithm
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
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An ant deposits pheromone along the path that it travels and is more likely to choose a path with a higher concentration of pheromone. The sensing and dropping of pheromone makes it easy to understand the trail forming behavior of ants. The reinforcement tendency of pheromone following behavior ensures selection of the shortest path from a set of paths. The reinforcement tendency of pheromone following behavior also ensures a biased selection of the initially followed paths over a path, which is shorter but discovered through chance at a later point in time. Under what conditions and limits can this initial bias be reversed? In this paper, we answer this question based on a theoretical analysis of the trail forming behavior of ants. We believe our results to contribute to the overall area of understanding how to build scalable systems that evolve to solve complex problems (e.g. point covering or the traveling salesman problem) without the necessity of central command-and-control.