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 beenaddressed by various researchers in the context of scheduling problems. various articialintelligence (ai) techniques have also been used to investigate and improve the pheromonetrail in worker assignment issues at the workshop oor level. this paper proposes a novelway of investigating and analyzing the issue of pheromone assignment through the neuralaugmented ant colony optimization (naaco) technique. the technique thus developedhas its roots in combining the strengths of articial neural networks (ann) and the extraordinary convergence capabilities of ant colony optimization (aco), thus, formulatingnaaco (neural augmented aco). a set of one hundred problems has been taken andan extensive methodology has been formulated to address the issue of pheromone updatesin worker assignments on these problems. the results have been formulated and areas offuture research have also been indicated.