A novel ACO algorithm for optimization via reinforcement and initial bias

Abstract

n this paper, we introduce the MAF-ACO algorithm, which emulates the foraging behavior of ants found in nature. In addition to the usual pheromone model present in ACO algorithms, we introduce an incremental learning component. We view the components of the MAF-ACO algorithm as stochastic approximation algorithms and use the ordinary differential equation (o.d.e.) method to analyze their convergence. We examine how the local stigmergic interaction of the individual ants results in an emergent dynamic programming framework. The MAF-ACO algorithm is also applied to the multi-stage shortest path problem and the traveling salesman problem.