Properties (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
algorithm
|
| gptkbp:appliesTo |
graph problems
|
| gptkbp:developedBy |
gptkb:Marco_Dorigo
|
| gptkbp:hasCitations |
scalability issues
local optima parameter sensitivity |
| gptkbp:hasContent |
traditional optimization methods
|
| gptkbp:hasRelatedPatent |
image processing
machine learning job scheduling load balancing data mining network routing vehicle routing |
| https://www.w3.org/2000/01/rdf-schema#label |
Ant Colony Optimization
|
| gptkbp:inspiredBy |
ant behavior
|
| gptkbp:isCharacterizedBy |
exploration and exploitation
|
| gptkbp:isEvaluatedBy |
convergence speed
solution quality computational efficiency |
| gptkbp:isLocatedIn |
gptkb:Java
gptkb:C++ Python |
| gptkbp:isPartOf |
swarm intelligence
genetic algorithms simulated annealing particle swarm optimization |
| gptkbp:isPopularIn |
academic research
industry applications optimization competitions |
| gptkbp:isRelatedTo |
combinatorial optimization
heuristic methods metaheuristic algorithms bio-inspired computing |
| gptkbp:isStudiedIn |
artificial intelligence
computer science mathematics operations research |
| gptkbp:isUsedFor |
other metaheuristics
|
| gptkbp:isUsedIn |
logistics
telecommunications finance transportation robotics bioinformatics energy management |
| gptkbp:mayHave |
traveling salesman problem
|
| gptkbp:requires |
parameter tuning
|
| gptkbp:usedFor |
solving optimization problems
|
| gptkbp:utilizes |
pheromone trails
|