Abstract
Ant Colony Optimization (ACO) has been successfully applied to those combinatorial optimization problems which can be translated into a graph exploration. Artificial ants build solutions step by step adding solution components that are represented by graph nodes. The existing ACO algorithms are suitable when the graph is not very large (thousands of nodes) but is not useful when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it. In this paper we study a new ACO model that overcomes the difficulties found when working with a huge construction graph. In addition to the description of the model, we analyze in the experimental section one technique used…
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2Topics & keywords
Topics
Keywords
- Ant colony optimization algorithms
- Computer science
- Graph
- Software
- Theoretical computer science
- Ant colony
- Mathematical optimization
- Artificial intelligence
UN Sustainable Development Goals
- Industry, innovation and infrastructure
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