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A CONCISE OVERVIEW OF APPLICATIONS OF AN

THOMAS ST ¨UTZLE, MANUEL L ´OPEZ-IB ´A ˜NEZ

2026enant colony optimizationmetaheuristicscombinatorial optimizationrouting problemsschedulingmachine learning

Abstract

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Ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants, primarily used for solving complex combinatorial optimization problems. This article provides a concise overview of the multitude of applications of ACO algorithms, which have shown effectiveness in addressing NP-hard problems among others. ACO involves artificial ants constructing candidate solutions through stochastic decisions influenced by pheromone trails, which are updated during execution to direct the search towards high-quality solutions. The applications range from routing problems to multi-objective optimization, extending to dynamic and continuous decision variable scenarios. The review follows a classification system based on the works of Dorigo and Stützle while incorporating numerous recent examples, highlighting the expansive reach of ACO beyond its initial scope. Given the large volume of current ACO applications, this overview necessarily remains incomplete but serves as a foundation for understanding the versatility and effectiveness of the ACO paradigm in various optimization contexts.

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Cite This Work

@article{173611b5-6cac-4bad-99e2-bb8d7d6d2f8b,
  title={A CONCISE OVERVIEW OF APPLICATIONS OF AN},
  author={THOMAS ST ¨UTZLE and MANUEL L ´OPEZ-IB ´A ˜NEZ},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - A CONCISE OVERVIEW OF APPLICATIONS OF AN
AU  - THOMAS ST ¨UTZLE
AU  - MANUEL L ´OPEZ-IB ´A ˜NEZ
PY  - 2026
LA  - en
ER  -

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