Intelligent pheromone up gradation mechanism through Neural augmented Ant Colony Optimization (NaACO) meta heuristic in machine scheduling

The pheromone update phase in Ant Colony Optimization (ACO) has been addressed by various researchers in the context of scheduling problems. Various Articial Intelligence (AI) techniques have also been used to investigate and improve the pheromone trail in worker assignment issues at the workshop oor level. This paper proposes a novel way of investigating and analyzing the issue of pheromone assignment through the Neural augmented Ant Colony Optimization (NaACO) technique. The technique thus developed has its roots in combining the strengths of Articial Neural Networks (ANN) and the extra ordinary convergence capabilities of Ant Colony Optimization (ACO), thus, formulating NaACO (Neural Augmented ACO). A set of one hundred problems has been taken and an extensive methodology has been formulated to address the issue of pheromone updates in worker assignments on these problems. The results have been formulated and areas of future research have also been indicated.