The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
ACM Computing Surveys (CSUR)
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Ant-Based Clustering and Topographic Mapping
Artificial Life
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Editorial survey: swarm intelligence for data mining
Machine Learning
| Hi-index | 0.00 |
This paper presents a work inspired by the Pachycondyla apicalis ants behavior for the clustering problem. These ants have a simple but efficient prey search strategy: when they capture their prey, they return straight to their nest, drop off the prey and systematically return back to their original position. This behavior has already been applied to optimization, as the API meta-heuristic. API is a shortage of api-calis. Here, we combine API with the ability of ants to sort and cluster. We provide a comparison against Ant clustering Algorithm and K-Means using Machine Learning repository datasets. API introduces new concepts to ant-based models and gives us promising results.