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Ant Colony Optimization

Marco Dorigo, Thomas Stützle

2026enant colony optimizationmetaheuristicscombinatorial optimizationroutingschedulingmachine learning

Abstract

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This book presents an in-depth exploration of Ant Colony Optimization (ACO), a novel computational technique inspired by the foraging behaviors of ants, which has become a significant method for solving complex optimization problems. The objective of this work is to elaborate on the principles of ACO and to provide practical applications for various optimization challenges. The methodology employed encompasses a theoretical analysis of ant behavior, development of ACO algorithms, and detailed experimental evaluations, particularly focusing on challenges like the Traveling Salesman Problem and other NP-hard problems. Results indicate that ACO provides efficient solutions to complex optimization problems and highlights the algorithm's flexibility and adaptability. The findings suggest that ACO is a robust tool that can be integrated into a variety of domains, thus contributing to advancements in both theoretical and practical aspects of optimization. Specific frameworks for implementing ACO algorithms are discussed, along with comparisons to other optimization techniques.

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Cite This Work

@article{b8ac466d-5ff7-432e-8ec5-f8a805a11344,
  title={Ant Colony Optimization},
  author={Marco Dorigo and Thomas Stützle},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Ant Colony Optimization
AU  - Marco Dorigo
AU  - Thomas Stützle
PY  - 2026
LA  - en
ER  -

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