In ant algorithms, each individual ant makes decisions according to the greedy force (short term profit) and the trail system based on the history of the search (information provided by other ants). Usually, each ant is a constructive process, which starts from scratch and builds step by step a complete solution of the considered problem. In contrast, in Ant Local Search (ALS), each ant is a local search, which starts from an initial solution and tries to improve it iteratively. In this paper are presented and discussed successful adaptations of ALS to different combinatorial optimization problems: graph coloring, a refueling problem in a railway network, and a job scheduling problem.