<p>This article proposes Ant Colony Optimization (ACO) algorithms for permutation flowshops with the objective of minimizing squared completion time differences variability of jobs. Minimizing the considered objective achieves uniformity of jobs flow, i.e., a rhythmic release of jobs, with almost equal inter-release time lapses between successive job releases, from the last machine in the flowshops. The objective considered appears to be essential when flowshops are components at the initial stages of manufacturing and assembly systems network. Variability in these flowshops propagates to the further stages in the network. If the high variability in the jobs flow is not regulated to lower levels, the manufacturing/assembly systems network deteriorate in the selected performance measures under the corrupting influence of variability propagation. The work contributes to the design and development of ACO algorithms for the problem considered in two ways. One with the development of four different ant-search-structures and another with the introduction and development of eight new Search Procedures (SPs) for local improvement. Twelve different variants of ACO algorithms are proposed, some with the composition of literature-based SPs and ant-search-structures inspired from literature and the remaining with the composition of the introduced SPs and ant-search-structure. Computational work with benchmark problem instances show that the variants composed of the introduced SPs and ant-search-structure perform well for considerable number of problem instances.</p>