abstract
- © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Ant colony optimization (ACO) is one of the most representative metaheuristics derived from the broad concept known as swarm intelligence (SI) where the behavior of social insects is the main source of inspiration. Being a particular SI approach, the ACO metaheuristic is mainly characterized by its distributiveness, flexibility, capacity of interaction among simple agents, and its robustness. The ACO metaheuristic has been successfully applied to an important number of discrete and continuous single-objective optimization problems. However, this metaheuristic has shown a great potential to also cope with multi-objective optimization problems as evidenced by the several proposals currently available in that regard. This chapter is aimed at describing the most relevant and recent developments on the use of the ACO metaheuristic for solving multi-objective optimization problems. Additionally, we also derive a refined taxonomy of the types of ACO variants that have been used for multi-objective optimization and we include a review of some of their real-world applications. In the last part of the chapter, we provide some potential paths for further research in this area.