Name
Abstract | ||
|---|---|---|
Ant Colony Optimization (ACO) differs substantially from other meta-heuristics such as Evolutionary Algorithms (EA). Two of
its distinctive features are: (i) it is constructive rather than based on iterative improvements, and (ii) it employs problem
knowledge in the construction process via the heuristic function, which is essential for its success. In this paper, we introduce
the ACO encoding, which is a self-contained algorithmic component that can be readily used to make available these two particular features of ACO to any search algorithm for continuous spaces based on iterative improvements to solve combinatorial optimization problems.
|
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15461-4_53 | ANTS - Ant Colony Optimization and Swarm Intelligence |
Keywords | Field | DocType |
combinatorial optimization problem,ant colony optimization,distinctive feature,aco encoding,heuristic function,continuous space,particular feature,construction process,evolutionary algorithms,iterative improvement,evolutionary algorithm,computer programming,search algorithm | Ant colony optimization algorithms,Mathematical optimization,Search algorithm,Evolutionary algorithm,Computer science,Constructive,Artificial intelligence,Optimization problem,Machine learning,Computer programming,Metaheuristic,Encoding (memory) | Conference |
Volume | ISSN | ISBN |
6234 | 0302-9743 | 3-642-15460-3 |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Moraglio | 1 | 463 | 40.85 |
| Fernando E. B. Otero | 2 | 306 | 21.29 |
| Colin G. Johnson | 3 | 933 | 115.57 |