Ant-based and swarm-based clustering

By Julia Handl, Bernd Meyer. 2007

169 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Laith Mohammad Abualigah
2. Essam Said Hanandeh
3. Ahamad Tajudin Abdul Khader
4. Adrian Šošić
5. Maximilian Hüttenrauch
6. Gerhard Neumann
7. Ganapati Panda
8. Satyasai Jagannath Nanda

In Statistical and Nonlinear Physics

1. Ahamad Tajudin Abdul Khader
2. Essam Said Hanandeh
3. Gerhard Neumann
4. Maximilian Hüttenrauch
5. Adrian Šošić
6. Laith Mohammad Abualigah

Citations in the corpus

• Deep Reinforcement Learning for Swarm Systems (2018, methodology): “Rule-based approaches use heuristics inspired by natural swarm systems, such as ants or bees [Handl and Meyer, 2007].”
• A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “In the survey article [199] both the approaches of ACO along with other swarm based clustering approaches (like bird flocking”
• Hybrid clustering analysis using improved krill herd algorithm (2018, methodology): “Two methods using ACO-based clustering were reviewed [50].”
• A Comprehensive Survey of Clustering Algorithms (2015, unknown): “Typical algorithms include the 4 main categories: ACO_based [96,97], PSO_based [97,98], SFLA_based [99] and ABC_based [100].”