Ant colony optimization (ACO) is a meta-heuristic based on the indirect communication of a colony of artificial ants mediated by pheromone trails with the collaboration and knowledge-sharing mechanism during their food-seeking process. In this study, we introduce two new features that are inspired from real ant behavior to develop a new ACO algorithm to produce better solutions. The proposed ACO algorithm is applied to two NP-hard flowshop scheduling problems. The first problem is to minimize the total completion time and the second is to minimize a combination of makespan and total completion time. Numerical results indicate that the proposed new features of ACO are very effective and the synergy of combining all the new features for the proposed ACO algorithm can solve the two problems to a certain scale by producing schedules of better quality.